Abstract: In order to make full use of the transfer knowledge between multiple tasks and reduce the computational cost, a multiple objective and multiple factor evolutionary algorithm based on dynamic resource allocation strategy is proposed. The multiple objective and multiple task optimization problem was decomposed into several single objective optimization sub-problems, and all single objective sub-problems were optimized simultaneously by using a single population with multiple factor distribution in a unified space. A dynamic resource allocation strategy in multiple factor environment was proposed, which allocated computing resources according to the evolution speed of these single objective sub-problems in each generation. The algorithm was applied to nine multiple task optimization problems. The experimental results show that the proposed algorithm can improve the optimization effect and reduce the computational cost.