Adversarial dynamics are a critical facet within the cyber security domain, in which there exists a co-evolution between attackers and defenders in any given threat scenario. While defenders leverage capabilities to minimize the potential impact of an attack, the adversary is simultaneously developing countermeasures to the observed defenses. In this study, we develop a set of tools to model the adaptive strategy formulation of an intelligent actor against an active cyber defensive system. We encode strategies as binary chromosomes representing finite state machines that evolve according to Holland's genetic algorithm. We study the strategic considerations including overall actor reward balanced against the complexity of the determined strategies. We present a series of simulation results demonstrating the ability to automatically search a large strategy space for optimal resultant fitness against a variety of counter-strategies.