AbstractTo promote precision installation accuracy of engineering structural components and improve construction quality, point cloud data obtained by 3D laser scanning are widely used in construction quality monitoring. Reasonable parameters are very important to control errors and improve efficiency during the process of 3D laser scanning. Therefore, this paper proposes a random forest-nondominated sorting genetic algorithm (RF-NSGA-II) multiobjective optimization model with an elite strategy. To study the relationship between 3D laser scanning parameters and measurement errors or efficiency, the best scanning parameters are identified. In this paper, high-precision prediction of the relative error and scanning time by 3D laser scanning parameters is achieved by using an RF, and the nonlinear mapping relationship function is obtained, which is used as the objective optimization function. The RF-NSGA-II multiobjective optimization algorithm is developed to optimize the relative error and scanning time, and the scanning time is the shortest under the condition of reasonable relative error. Through this study, we can mainly draw the following conclusions: (1) based on the RF, we can obtain a prediction model of relative error and scanning time with high accuracy, in which the R2 (determination coefficient) value and root mean square error (RMSE) of the relative error prediction model are 0.967 and 0.0277, respectively, and the R2 value and RMSE of the scanning time prediction model are 0.978 and 0.0243, respectively; (2) optimized design parameters for 3D laser scanning of structural components are obtained, including a horizontal incident angle of 90°, an inclination angle of 90°, point cloud density of 3.2 mm, measurement distance of 3 m, resolution of 0.456, and visibility of 8.67 km; and (3) the RF-NSGA-II model developed can effectively reduce the relative error of the 3D laser (by 2.16 mm) and shorten the scanning time (by 148.926 s) compared with the average value. The structural components also meet the requirements in the deviation test. Therefore, the application of the RF-NSGA-II model in the assembly and fabrication of structural components can realize the intellectualization of the production process and improve the precision of the prefabrication of structural components, which has high engineering application value.