To enhance the quality of TC4 titanium alloy cladding coating, a hybrid multi-objective optimization model integrating response surface methodology (RSM) and the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) was developed. The model focused on optimizing the laser power, feeding speed, and scanning speed as factor, with the objectives of minimizing the dilution rate, maximizing the aspect ratio, and enhancing microhardness. The regression model of the relationship between input and response based on RSM was constructed, and the reliability of the variance analysis model was used. Then NSGA-Ⅱ algorithm was used to realize the multi-objective optimization of the process. The results show that the optimal combination of process parameters is as follows: laser power 2 250 W, scanning speed 19 mm/s, powder feeding rate 1.3 r/min. The optimization resulted in substantial improvements, with a 13.2% reduction in the dilution rate, a 7.1% increase in the aspect ratio, and a 6.4% enhancement in microhardness.