To solve the increasing run-time complexity with the growth of population solutions in PESA, an evolutionary algorithm of multi-objective optimization, we present a comentropy- based PESA algorithm (C-PESA). With this algorithm, the gradual development and maturity of the solution sets can be observed with the continuous calculation of entropy values to determine whether to stop the optimization process or not, which reduces the run-time complexity to some extent. Simulation results show that the computational effort of C-PESA increases at a linear order with the increasing number of solutions, and the efficiency of evolutionary algorithm shows improvement.