The University of Iowa has successfully developed Reliability-Based Design Optimization (RBDO) method and software tools by utilizing the sensitivity analysis of the fatigue life; and applied RBDO to Army ground vehicle components to obtain reliable optimum designs with significantly reduced weight and improved fatigue life. However, this method cannot be applied to broader Army application problems due to lack of sensitivity analysis in many application areas. Thus, for broader Army applications, a sampling-based RBDO method using surrogate model has been developed recently. The Dynamic Kriging (DKG) method is used to generate surrogate models, and a stochastic sensitivity analysis is used to compute the sensitivities of probabilistic constraints with respect to independent and correlated random variables. Once the DKG method accurately approximates the responses, there is no further approximation in the estimation of the probabilistic constraints and stochastic sensitivities, and thus the sampling-based RBDO can yield very accurate optimum design. For computational efficiency of the sampling-based RBDO method for large-scale engineering problems, a parallel computing is proposed. Numerical examples verify that the proposed sampling-based RBDO finds the optimum designs very accurately and efficiently.