The availability of efficient and accurate metamodel for optimization computation is crucial to the success of applications of robust optimization of computationally intensive simulation models. To address this need, a framework has been presented for robust optimization on problems that involve high dimensional. The framework was combined with support vector regression (SVR) approximation model and a genetic algorithm (GA). The performances of SVR were compared with those of polynomial regression (PR), Kriging and back-propagation neural networks (BPNN). The results showed that the prediction accuracy of SVR model was higher than those of others metamodels. The applicability of the algorithm developed by combining SVR and GA was demonstrated by using a two-bar structure system study, and was found to be accurate and efficient for robust optimization. The optimization framework was effectively utilized to achieve a potential performance improvement.