To solve the problems of low calibration accuracy, inferior repeatability, and weak robustness in conventional camera calibration, an optimized camera calibration method based on an improved grey-wolf optimization algorithm is proposed. This method improves the population initialization, linear convergence factor, and position update strategy of the grey-wolf algorithm, as well as integrates search strategies based on dimension learning and improved selection, crossover, and mutation operators to optimize camera calibration parameters. First, the MATLAB calibration toolbox is used to extract the corner points of the calibration board image. Based on the camera calibration principle, the corresponding relationship between the corner point coordinates of the calibration board and the coordinates of three-dimensional points in space is established to obtain the initial estimation of the camera internal parameters and distortion coefficients. Accordingly, the optimization parameters to be optimized are set. Second, based on the initial estimation, an initial population for the grey-wolf genetic algorithm is generated within the optimization range. Next, an average reprojection-error equation is constructed, with the objective function of minimizing this error. The improved grey-wolf genetic optimization algorithm is used to optimize the calibration parameters. Finally, the method is experimentally compared with other optimization methods. The results show that the camera calibration method based on the improved grey-wolf genetic algorithm not only has the smallest average reprojection error but also exhibits the best repeatability and robustness.