Evolutionary algorithms (EA) are recently used to explore the parameter space of complex system simulations as the methodology sees models as black boxes. The first advantage is that these algorithms become independent from what kind of simulation has to be explored. The task is finding the parameter settings to optimize a given objective function. This optimization process evaluates the performance of possible parameter sets and converges towards the best alternatives. The evaluation needs to launch hundreds of thousands of simulation runs. This procedure copes with the combinatorial explosion of computation time and requires considerable computational resources. Furthermore, the original algorithms cannot guarantee the exploration in the search space uniformly and equally because the search is probabilistic. This work elaborates a multistage optimization process in a grid-enabled modeling and simulation platform. We propose a hybrid integration of various continuous optimization algorithms and optimize them for running with different Distributed Resource Management (DRM) systems. The performance of algorithm is compared to original algorithm in the optimization of Ants model.