Aiming at the path planning problem of manipulator with complex static background and multiple constraints, a new multi-population particle swarm optimization algorithm based on elite population and monocular vision was proposed. Firstly, the image difference algorithm was used to eliminate the background, then the contour surrounding method was used to find out the target area, and the model pose estimation method was used to locate the target position. Secondly, a multi-population particle swarm optimization based on elite population was proposed to obtain the optimal angles of the manipulator according to the target position. In this algorithm, the elite population and the sub-populations were combined to form the multi-population particle swarm, and the pre-selection and interaction mechanisms were used to make the algorithm jump out of local optimums. The simulation results show that compared with the real coordinates, the coordinates error of the object position obtained by background elimination method is small; compared with those of the state-of-the-art evolutionary algorithms, the average fitness values of the paths and the Mean Square Errors (MSE) obtained by the proposed algorithm are the smallest for the objects in different positions.