Abstract: To better solve the multi-objective optimization problems in arithmetic algorithms, a multiobjective quantum arithmetic optimization algorithm based on archive sets is proposed and tested on seven benchmark functions. By analyzing the test results and comparing them with the commonly used multi-objective optimization methods/multi-objective particle swarm optimization algorithms, fast non-dominated sorting genetic algorithms, and multi-objective grey wolf optimization algorithms, it is shown that this algorithm is superior to the aforementioned commonly used multi-objective optimization algorithms, with a higher convergence rate.