This thesis proposes a novel algorithm to optimize multi-modal, nonlinear, black-box objective functions for electric power system design using an electromagnetic transients (EMT) simulator. The algorithm discovers multiple local optimal solutions for a given complex power system, and then generates accurate surrogate models of an objective function around each discovered local optimal solution. These surrogate models represent the local behaviour of the objective function that can be used in the subsequent stages of sensitivity analyses. Using surrogate models instead of intensive transient simulation during sensitivity analysis reduces computational intensity and simulation time. This makes the proposed algorithm particularly suited for optimization of computationally expensive black-box functions. The stages of the algorithm can be implemented independently and hence the computations can be done in parallel. Therefore, the algorithm is implemented in a parallel environment to gain significant speed-up in the design of electric power systems. Comparative studies in terms of objective function evaluation and computation time are provided. Using several multi-modal benchmark objective functions, the superiority of the proposed algorithm compared to other recently developed algorithms is demonstrated. Additionally, the application of the algorithm in the design process of complex electric power system demonstrated through several examples. The case studies show that the parallelized algorithm provides computational savings up to 39 times compared to the conventional sequential approach. / May 2016