To improve the measurement accuracy and efficiency of self-driven arm-articulated coordinate measuring machines （AACMMs）， a trajectory optimization method based on the particle swarm optimization algorithm is proposed to solve the online trajectory optimization problem of AACMMs. First， the forward kinematics model of the measuring machine is established using the MDH parameter method， and mixed trajectory planning is performed via S-shape addition and subtraction and uniform linear interpolation. Second， particle swarm optimization is used to optimize the trajectory by considering the operating time and motion stability as the optimization objectives. Finally， a measuring machine model is established using MATLAB and ADAMS to analyze a standard ball measurement numerically. The standard ball measurement is performed using a prototype of the measuring machine. The results reveal that the hybrid trajectory planning optimization method based on the particle swarm optimization algorithm can ensure the smooth operation of the measuring machine. Additionally， the measuring time of the standard ball is reduced from 62.91 to 57.35 s， and the measuring radius error of the standard ball is reduced from 0.057 1 to 0.042 3 mm. Hence， the trajectory optimization method can effectively reduce probe vibrations and improve the measurement accuracy and efficiency of the measuring machine.