Kinematics based guidance laws like the proportional navigation (PN) and many other linear optimal guidance laws perform well in near-collision course conditions. These have been studied thoroughly in the literature from all aspects, ranging from optimality to capturability, for planar or two dimensional interceptor-target engagements, and to a lesser extent, for three dimensional engagements. But guidance in widely off-collision course conditions like high initial heading errors has been relatively less studied. This is probably due to the inherently high nonlinearity of the problem, which makes it a far more difficult problem to solve. However, with increasing speed and agility of interceptors and targets, solutions of such problems have acquired an increased urgency, as has been reflected in the recent literature. This thesis proposes a guidance law based on differential evolution (DE), a member of the evolutionary algorithms (EA) family. While EAs have been applied extensively to static optimization problems, they have been considered unsuitable for solving dynamic optimization or optimal control problems, due to their computationally intensive nature, and their consequent inability to produce solutions online in real-time, except for systems with very slow dynamics. This thesis proposes an online-implementable optimal control for interceptor guidance, a problem with inherently fast dynamics. The proposed law is applicable to all initial geometries including those that involve high to very high heading errors. While interception by itself is a challenging task in the presence of high heading errors, an additional requirement of optimality is also imposed. The first part of the thesis considers only the 2-D kinematic model with high heading errors. In the second part, a 3-D realistic dynamic model, which includes a time-varying interceptor speed, thrust, drag and mass, besides gravity in the vertical plane of motion, and upper bound on the lateral acceleration, is considered, in addition to high heading errors. It is shown that the same structure of the law that is proposed for the 2-D kinematic model can also be used for the 3-D realistic model, if the rest of the complexities of moving from 2-D space to 3-D space, and from kinematics to dynamics is duly addressed. The guidance law proposed does not require time-to-go, the estimation of which can be a difficult problem in high heading error scenarios in which the closing velocity can be negative. Easy to compute and simple to implement in practice, the proposed law does not need any of the techniques or methods from classical optimal control theory, which are complicated and suffer from several limitations. The empirical pure PN (PPN) law is augmented with a term that is a polynomial function of the heading error. The values of the coefficients of the polynomial are found by using the DE. The computational effort required for this low dimensional polynomial optimization problem is shown to be low enough to enable online implementation in real-time. The performance of the proposed law in nominal and off-nominal conditions is validated through several simulations for the 2-D kinematic model, and the 3-D realistic dynamic model. The results are compared with the PPN, augmented PPN and the all-aspect proportional navigation (AAPN) laws in the literature, as per several criteria like optimality, peak latax required and robustness to off-nominal conditions. A successful online implementation of the proposed law for application in practice is also demonstrated. An obvious limitation of optimization by soft computation methods like differential evolution is that no rigorous proof of either convergence or optimality exists for such methods. A fallback option in the form of a conventional guidance law is included in the scheme in case of failure of convergence, and an indirect proof of optimality is provided in the third and final part of the thesis. The same guidance problem is solved by direct multiple shooting method, and it is shown that the numerical results of the two methods compare favourably. The solution by the shooting method is optimal, but computationally far more intensive and takes a computation time of an order of magnitude that is at least one or two times that of the simulation time of the plant. It also needs a good initial guess solution that lies within the region of convergence, which can be a difficult task by itself. Moreover, the shooting method solution is only open loop, and hence applicable for the given model and initial conditions only. Whereas, the simplicity of the method proposed in the thesis makes the solution or guidance law computable in a fraction of the flight time of the engagement, thereby making it online implementable. Equally important, is the fact that it is closed loop, and hence robust to off-nominal conditions like variations in the plant model and parameters assumed in its design.