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Agent-based models can present special challenges to model calibration due in part to their high parameter count, tunable agent behaviors, complex emergent macrophenomena, and potentially long runtimes. However, due to this difficulty, these models are most often calibrated by hand, or with hand-coded optimization tools customized per-problem if at all. As simulations increase in complexity, we will require general-purpose, distributed model calibration tools tailored for the needs of agent-based models. In this paper, we present the results of a system we have developed which combines two popular tools, the MASON agent-based modeling toolkit, and the ECJ evolutionary optimization library. This system distributes the model calibration task over many processors, provides many stochastic optimization algorithms well suited to the calibration needs of agent-based models, and offers the ability to optimize not just model parameters but agent behaviors.