Abstract: To solve the process optimization problem of laser cladding under the interaction of multiple process parameters, iron-based alloy coatings were prepared on Q345B steel plates by laser cladding. A correlation model between laser power, scanning speed, powder feeding rate and coating hardness, dilution rate, and aspect ratio was established using the response surface methodology (RSM), and the effects of process parameters and their interactions on coating performance were analyzed. Process parameters were optimized using the non-dominated sorting genetic algorithm II (NSGA-II) and the technique for order preference by similarity to an ideal solution (TOPSIS) with consideration of coating performance and process economy. The results show that the interaction between scanning speed and laser power has a significant effect on hardness, and the interaction between scanning speed and powder feeding rate has a significant effect on dilution rate and aspect ratio. The optimized process parameters are as follows: laser power of 900 W, scanning speed of 447 mm/min, powder feeding rate of 0.67 r/min, and overlapping rate of 40%. The average hardness and height of the coating are 612 HV and 0.66 mm, respectively. Compared with the substrate, the average friction coefficient of the coating decreases by about 27%; the maximum wear depth decreases by about 76%, and the wear resistance is significantly improved. The combination of RSM and NSGA-II-TOPSIS can exert the advantages of rapid modeling of RSM, multi-objective optimization of NSGA-II, and comprehensive evaluation of TOPSIS, which provides a useful reference for the intelligent optimization of the laser cladding process.