An Improved Particle Swarm OptimizationSimulated Annealing Algorithm (IPSO-SAA) is proposed to enhance the measurement accuracy of the Coordinate Measuring Machine (CMM) for the measured object by identifying the optimal measurement area. Firstly, the distribution pattern of volumetric errors within the CMM measurement space is analyzed. Individual geometric error models are fitted using the least squares method, and an optimization model for the point errors in the CMM space is established. The proposed IPSOSAA method, which combines adaptive weighting, adaptive disturbance force and simulated annealing algorithm, performs better than conventional Particle Swarm Optimization (PSO) and Adaptive Particle Swarm Optimization (APSO) algorithms. Comparative experiments show that IPSO-SAA is superior to PSO and APSO algorithms in terms of the best, worst, mean, and standard deviation values, and the single optimization speed is increased by 45.1% and 29.2% respectively. The results obtained from the IPSO-SAA algorithm identification indicate that, based on the size of the planning optimization space being 30 mm×30 mm×30 mm, the optimal measurement area in the CMM identified by the IPSO-SAA algorithm is 206 mm≤X≤236 mm, 350 mm≤Y≤380 mm, -262 mm≤Z≤-232 mm. Comparative experiments with a high-precision standard ball, with a diameter of 15.8747mm and a sphericity of 50nm, demonstrate that when placed within the optimal measurement area in the CMM, the minimum diameter measurement error of the standard ball is 1.7μm, validating the correctness of the proposed method. The method presented in this study is universal and can be used to determine the optimal measurement area of CMM for other measured objects.