Simulation models are one of the most effective tools to study supply chains. Compared to analytical and mathematical programming techniques, they offer the capability to include a greater deal of fidelity. Different kinds of simulation models have been widely used to study various aspects of supply chain management. These models provide a very convenient approach to generate various "what-if" scenarios and find the optimal values of the discrete variables. However stand-alone simulation models cannot be used to optimize the continuous variables. It is necessary to couple the simulation model with an optimization approach in order to find the optimal values of the continuous variables. During the recent years, supply chains have evolved into global, highly complex networks and the overall supply chain operations are a result of numerous autonomous, adaptive and intelligent entities. A high fidelity simulation model is not only difficult to develop but also difficult to use in an optimization framework due to computational complexity involved in each function evaluation. In order to optimize the variables in these simulation models, deterministic optimization solvers cannot be used as the derivatives are unavailable. Also, since the simulations take long times to run, it is not possible to perform a large number of simulation runs in order to approximate the derivatives. Taking these factors into consideration, we propose a surrogate based derivative free optimization methodology to solve a supply chain planning problem.