Rotorcraft optimization is a challenging problem due to its conflicting requirements among many disciplines and highly coupled design variables affecting the overall design. Also, the design process for a composite rotor blade is often ambiguous because of its design space. Furthermore, analytical tools do not produce acceptable results compared with flight test when it comes to aerodynamics and aeroelasticity unless realistic models are used, which leads to excessive computer time per iteration. To comply these requirements, computationally efficient yet realistic tools for rotorcraft analysis, such as VABS and DYMORE were used as analysis tools. These tools decompose a three-dimensional problem into a two-dimensional cross-sectional and a one-dimensional beam analysis. Also, to eliminate the human interaction between iterations, a previously VABS-ANSYS macro was modified and automated. The automated tool shortened the computer time needed to generate the VABS input file for each analysis from hours to seconds. MATLAB was used as the wrapper tool to integrate VABS, DYMORE and the VABS-ANSYS macro into the methodology. This methodology uses Genetic Algorithm and gradient-based methods as optimization schemes. The baseline model is the rotor system of generic Georgia Tech Helicopter (GTH), which is a three-bladed, soft-in-plane, bearingless rotor system. The resulting methodology is a two-level optimization, global and local. Previous studies showed that when stiffnesses are used as design variables in optimization, these values act as if they are independent and produce design requirements that cannot be achieved by local-level optimization. To force design variables at the global level to stay within the feasible design space of the local level, a surrogate model was adapted into the methodology. For the surrogate model, different ``design of experiments" (DOE) methods were tested to find the most computationally efficient DOE method. The response surface method (RSM) and Kriging were tested for the optimization problem. The results show that using the surrogate model speeds up the optimization process and the Kriging model shows superior performance over RSM models. As a result, the global-level optimizer produces requirements that the local optimizer can achieve.