Abstract: The dynamic characteristics of a horizontal five-axis machining center are the core factors determining its machining accuracy, accuracy stability, and machining efficiency. However, the mapping relationship between the mass distribution of key structural components and the overall dynamic characteristics of the machine is not yet clear. To this end, a mass matching optimization method based on a multi-objective genetic algorithm is proposed, aiming to systematically improve the dynamic performance of the machine tool. Firstly, by establishing a parametric model of the machine tool and combining Box-Behnken experimental design, a response surface model is constructed for the relationship between the mass of key components and the first three natural frequencies of the entire machine. Secondly, with component mass as the design variable and the first three natural frequencies as the optimization objectives, a multi-objective genetic algorithm is used for optimization to obtain the optimal solution set for mass distribution. The optimization results show that under the condition that the total mass change does not exceed 20%, the first three natural frequencies of the optimized machine tool are increased by 11.1%, 11.1%, and 11.6%, respectively, and the overall dynamic performance of the machine tool is significantly improved.