Today, although laser engraving technology is widely used in 2D image engraving, when the image is larger and more complicated, most existing algorithms for engraving path planning have a huge computational burden and reduced engraving efficiency. Accordingly, this article addresses the trajectory optimization problem in large-scale image engraving. First, we formulate the problem as an improved model based on the large-scale traveling salesman problem (TSP). Then, we propose a three-layered algorithm called 3L-MFEA-MP, structured as follows: an upper layer, the genetic algorithm (GA); a middle layer, the GA; and a bottom layer, the parallel multifactorial evolutionary algorithm. Experiments on four classic large-scale TSP datasets show that our algorithm exhibits superior performance in terms of the path length and engraving time compared with other algorithms. In particular, compared with the single-thread algorithm, the proposed parallel algorithm reduced the engraving time by 80%. Moreover, the engraving machine experiment demonstrated that the engraving time of our algorithm on mona-lisa 100K, vangogh 120K, and venus 140K was approximately one tenth that of the traditional dot engraving method. The results indicate that the proposed algorithm can reduce the computational burden and improve engraving efficiency in engraving path planning.