To solve the problem that the calibration accuracy of internal parameters of the camera in the large-size single-camera vision system has great influence on the overall measurement accuracy, this paper presents a virtual stereo calibration method for the internal parameters of the camera based on the Mutation Mechanism Particle Swarm Optimization (MMPSO) algorithm. The method is based on a two-stage optimal strategy. Firstly, a camera imaging model is established to estimate the initial values of the external parameters and some internal parameters. Then, the internal parameters are optimized and calibrated by the MMPSO algorithm to determine the final result. To provide accurate calibration control points, a calibration hardware platform was built. An infrared light-emitting diode was fixed on the measuring head of a three-coordinate measuring machine (CMM), which drove the diode to move, and a large-space virtual three-dimensional calibration board was constructed. The experimental results showed that all of the 10 main internal parameters reached the order of magnitude requested by the measurement accuracy, which validated the effectiveness of the method. The results of two calibration methods were measured by equidistant measurement with the single-camera vision coordinate measurement system. The population standard deviation of the three-dimensional calibration method of Janne Heikkila was 0.112 mm, but the population standard deviation of the virtual stereo calibration method based on the MMPSO algorithm was 0.084 mm. The comparison of the standard deviations of the measured data proves that the proposed calibration method is more stable and accurate. This method can meet the requirements of the large-space single-camera vision measurement system for the accuracy of camera parameters, and it has a certain guiding effect on nonlinear optimization problems such as camera calibration in the field of visual coordinate measurement technology.