A multi-objective evolution algorithm based oil field stimulation measure programming is presented in this paper. Stimulations are very important measure for mature oil field to maintain stable oil yield. Stimulation measure programming can reduce cost and increase economical profit. Ex-ante and ex-post wavelet neural network models for oil well or block production was constructed first. Then predict models based stimulation measure programming models was constructed. These models are usually constrained multi-objective optimizations. Obtained Pareto optimal solutions using multi-objective evolution algorithm are used for ex-ante decision support and ex-post evaluation. Multi-objective evolution algorithm was used to obtain Pareto optimal set of oil field stimulation measure programming. All of oil well's stimulation serial-number is encoded into an integer array as chromosome. Population consists of feasible chromosomes. Then two aggregated fitness measures are used to evaluate each individual's fitness, one is based on dominant count to achieve proximity, another is based on distance to maintain population's diversity.