In the garment production industry, garment cutting size matching plan is an important step in the process, which plays a decisive role in production management and cost control. In this paper, we first model the size matching problem of garment cutting, then use the improved fast particle swarm algorithm (APSO) to optimize the multi-objective optimization solution, and finally verify the performance of the APSO algorithm and the actual effect of garment size matching with cases. Comparing the test results of APSO, PSO and LDWPSO algorithms in the six test functions of Griewank, Ackle, Levy, Rastrign, Schwefel and Sphere, it can be seen that: with the improvement of the problem dimensions, the APSO algorithm used in this paper can still maintain a better optimization accuracy, and the optimization accuracy and stability are significantly improved compared with the PSO and the LDWPSO algorithms. LDWPSO algorithms. In the actual case, the APSO algorithm is more reasonable in the size combination and the number of layers of fabric, for four different types of apparel orders have obtained a superior optimal solution set, cutting production error is far less than the enterprise requirements. At the same time, compared with other optimization methods, the APSO algorithm has better optimization accuracy and solving efficiency, and can obtain a more superior cutting and bed splitting scheme. The algorithm proposed in this paper can effectively optimize the cutting size matching process, reduce fabric waste and production equipment investment, and has good application value and reference significance.