To automatically generate simulated scenarios an algorithm is needed to search for the optimal subset of scenario parameters. For most simulated environments the scenario search space is complex and populated with discontinuities, multimodality, and noise. Complexity is especially evident in networked simulations, where the search space can be enormous. Some high-fidelity, large scale network simulation may require specifications of millions of parameters to describe all entities at a high level of resolution. In this paper we present the application of the Genetic Algorithms search technique for scenario optimization in network simulations. Genetic Algorithms as optimization and adaptation techniques, maintain a constant-sized population of candidate solutions known as individual scenarios. At each iteration, known as a generation, each scenario is evaluated and recombined with others on the basis of its overall quality or fitness in solving the simulation task. New scenarios are created using two main genetic recombination operators known as crossover and mutation.