The application of spot welding robots to automobile body-in-white welding has greatly improved the production efficiency of automobiles. Multi-objective welding robot path planning focusing on path length and energyoptimization is solved. To solve the problem, after a new multi-objective particle swarm optimization algorithm (multi-objective partical swarm optimization algorithm based on three status coordinating searching, TC-MOPSO) is improved, a discrete multi-objective particle swarm optimization algorithm based on three status coordinating searching (DTC-MOPSO) is presented to solve the discrete multi-objective optimization problem. Compared with two classical multi-objective optimization algorithms, high competition in terms of convergence and diversity metrics of the DTC-MOPSO algorithm is proved. In addition, MATLAB toolbox robotics is used to analyze a robot's kinematics, inverse kinematics, and inverse dynamics to obtain the path length and energy consumption. The improved algorithm is used to optimize welding robot path planning, and the result is obviously superior to the other algorithms.