Abstract: The Lamarckian evolution learning strategy (LELS) and Darwinian evolution learning strategy (DELS) are discussed in terms of their similarities and differences in learning implementation. The former is based on inheritance of acquired character, i.e., both phenotype and genotype can be optimized through learning while the later only optimizes phenotype based on Darwinian selection. The convergence of ELS is proved using Markov chain theory. And we also theoretically demonstrate that DELS has stronger escaping capacity. These algorithms are applied to 8 standard test functions. Simulation results show that LELS and DELS yield faster convergence and better global optimization ability than standard evolution strategies; moreover, DELS also leads to better escaping capacity. Finally, limitations of the work as well as the future study are discussed.