In view of the fact that the genetic algorithm of stochastic programming based on random simulated technology has succeed greatly , this paper points out that changing parameters of genetic algorithm can obtain a sequence of optimum values of goal function . taking these genetic algorithm values as sampling data , we can get fitting optimum function by using multivariate spline regression and get the lipschitzs constant of the fitting optimum function . so for any chance constrained programming problem , we can get its interval estimate 鉴于基于随机模拟技术的遗传算法在求解随机规划问题上的优越性，本文指出，改变遗传算法的参数条件，在此基础上求得机会约束规划的若干个最优值，以这些最优值为样本点，利用多元样条回归，拟合得到最优值函数，进而求出最优值函数的lipschitzs常数，从而对于任一机会约束规划问题，都可以得到它的一个区间估计。
