Agent based modeling techniques can be used effectively to study complex systems, which have many parameters. The behavior of the system typically depends heavily on the values of these parameters. In the example of a complex system studied here, an ecosystem, there are some sets of parameters for which the system will be sustainable, i.e., in which the system's participating entities will not die off. When the number of parameters becomes large, the parameter space becomes very broad. Hence finding the optimum parameters for sustainability typically becomes an NP- hard problem. In these circumstances, an effective solution can be found by a combined application of agent-based modeling (to understand the behavior) and a genetic algorithm (for a quantitative prediction). An Agent Based Modeling framework is ideally suited for modeling these systems bottom-up, and genetic algorithms are search techniques well-suited for searching sets of optimal points in the parameter space through natural selection. Genetic algorithms running in parallel on a cluster of PCs theoretically give linear speed, leading to increased efficiency. The work presented here is divided into three phases-(i) development of an agent-based model for a complex system, an ecological food web (ii) search of the parameter space of the system using a genetic algorithm (iii) parallelization of the application. The food web was modeled using the simulation software, Swarm. This system was then integrated into a parallel genetic algorithm package PGAPack, to search for an optimal set of parameters. The resulting application was then measured for efficiency and speedup by running it on a cluster of workstations. The results obtained were very promising, in terms of successfully developing a sustainable system and obtaining increased performance through parallelization using the cluster.