A multi-objective artificial bee colony algorithm based on evolutionary knowledge integrated is proposed. The elite population knowledge and population itself knowledge are integrated to guide the evolution of employed bees and maintain the diversity and superiority of the population further. Then, a method of combining the individual dominance relationship and the population distribution relationship is introduced into the probability selection of onlooker bees. Finally, a more strict strategy of external file maintenance is proposed to reduce the cost of maintenance and improve the distribution performance of the solution set. Comparing with three kinds of multi-objective optimization algorithms in the standard functions, the results show that the proposed algorithm has good convergence performance and distributed performance, and the coverage of the solution set is more wider.