GAs, effective stochastic search algorithms based on the model of natural evolution and genetics, have been successfully applied to various optimization problems. When population size is not large, GAs often suffer from the phenomenon of premature convergence in which all chromosomes in the population lose the diversity of genes before they find the optimal solution. In this paper, we propose that a new heuristic that maintains the diversity of genes by adding some chromosomes with random mutation and selective mutation into population during evolution. And population size changes dynamically with supplement of new chromosomes. Experimental results for several test functions show that when population size is rather small and the length of chromosome is not long, this method is effective.