For the problem that an optimal scheme for step-down-stress accelerated degradation test is difficult to or cannot be obtained through analytical methods,an optimization algorithm of Monte-Carlo simulation step-down-stress accelerated degradation test (SDSADT) based on Particle Swarm Optimization (PSO) is presented.This algorithm generates test degradation data through a number of repeated simulation tests,and finds the optimal monitoring frequency,detection times and sample size.With the minimum logarithmic asymptotic variance estimation of 100pth percentile of the lifetime distribution of the product at use condition as the objective,a statistical analysis of the degradation test data is made by using Maximum Likelihood Estimation (MLE) theory based on PSO,and a model of optimization design for simulation based SDSADT is established. Based on an example,the optimal design scheme is given under different constraint conditions,with the conclusion that this method is also applicable to optimization design for SDSADT of small-subsample product,and the final optimal test scheme is obtained.