Reliability-based design optimization is often a very computationally expensive process that determines the best design that satisfies a set of constraints with a specified probability, given uncertainty in the inputs to the design. The Monte Carlo method used in this work to assess the uncertainty in a design given the input uncertainty is made computationally feasible through the use of kriging models as approximations to the original subsystem analyses. The re liability-based design optimization method described in this work uses Simulated Annealing to direct the optimization process. By using a kriging model as an approximation, additional uncertainty, namely model uncertainty, is incorporated into the design models and is included in the uncertainty assessment. During the reliability-based design optimization method described in this work, the number of samples used in the Monte Carlo simulation is controlled by the current temperature of the Simulated Annealing algorithm. More samples are used to improve precision as the solution nears the optimum. The method is demonstrated with the design of a satellite, and the results of not including and including the model uncertainty are presented.