Chemical process reliability has become more recognized both in terms of its impact on economics, and for providing academically challenging problems. In this work, we give an overview of some of the major challenges in formulating and optimizing preventive maintenance. As a result, we propose a general framework for preventive maintenance optimization that combines Monte Carlo simulation with a genetic algorithm.This proposed approach has distinct advantages. When applied to opportunistic maintenance problems, the method developed overcomes demonstrated shortcomings with analytic or Markov techniques in terms of solution accuracy, versatility, and tractability. The framework is easily integrable with general process planning and scheduling, and it provides sensitivity analysis. Furthermore, a genetic algorithm combines well with Monte Carlo simulation to optimize a non-deterministic objective function.