To enhance the searching capability with the limited sample scale, a sort of robust particle swarm optimization algorithm based on time-varying Sigmoid function is proposed. Quasi-Monte Carlo method is used to approximate the effective objective function. The selecting probability of different sample size in different iteration is designed based on the time-varying Sigmoid function, which is changed in the iteration process. In the prophase of the algorithm, the expected value of sample size is small, and then the exploration speed is accelerated. In the anaphase of the algorithm, the expected value of sample size is large, and then the exploitation precision is improved. The simulation results of standard test functions show that this method possesses better robust optimization capability.