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A new methodology is given in this paper to obtain a near-optimal strategy (i.e., specification of courses of action over time), which is also robust to environmental perturbations (unexpected events and/or parameter uncertainties), to achieve the desired effects. A dynamic Bayesian network (DBN)-based stochastic mission model is employed to represent the dynamic and uncertain nature of the environment. A genetic algorithm is applied to search for a near-optimal strategy with DBN serving as a fitness evaluator. The joint probability of achieving the desired effects (namely, the probability of success) at specified times is a random variable due to uncertainties in the environment. Consequently, we focus on signal-to-noise ratio (SNR), a measure of the mean and variance of the probability of success, to gauge the goodness of a strategy. The resulting strategy will not only have a high likelihood of inducing the desired effects, but will also be robust to environmental uncertainties.