Agent-based models can be a powerful tool for evaluating the impact of policy decisions on a population. However, analyses are traditionally beholden to one set of rules hypothesized at the conception of the model. Modelers must make assumptions of agent behavior that are not necessarily governed by data and the actual behavior of the true population can thusly vary. Evolutionary model discovery (EMD) seeks to provide a solution to this problem by leveraging genetic algorithms and genetic programming to explore the plausible set of rules that can explain agent behavior. Here we describe an initial use of the EMD system to develop robust policies in a resource constrained environment. In this instance, we extend the NetLogo implementation of the Epstein Rebellion model of civil violence as a sample problem. We use the EMD framework to generate 23 plausible populations and then develop policy responses for the government that are robust across the plausible populations.