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Aiming at shortcomings of existed design structure matrix based task programming methods, a new stochastic task programming model is built in which task execution time and cost are described as stochastic variable subjected to some type of probability distribution. In view of built task programming model, a hybrid simulation optimization algorithm is developed which adopts ordinal optimization and optimal computing budget allocation technique based genetic algorithm to perform local search in the framework of nested partitions method. Hybrid algorithm unites various advantages of genetic algorithm in powerful local search and nested partitions in global optimization. A task programming case study of rotor and bearing system validates that our task programming model and solving algorithm are efficient and effective.