In order to improve the efficiency and quality of laser paint removal, the surface roughness of the substrate after laser paint removal was optimized by response surface analysis. By establishing the quadratic polynomial regression model of laser paint removal process parameters, the adaptability of the model is verified, and the surface properties of the substrate after using the optimal parameters are further compared and analyzed. Results show that the laser power and frequency have significant effects on the surface roughness of the substrate, and scanning speed and scanning times have little effect on surface roughness. When using 2024 aluminum alloy as the base, the paint thickness is 100 μm, the optimal parameters obtained by response surface analysis method are: laser power 45 W, laser frequency 400 kHz, laser scanning speed 600 mm/s, scanning times 6. Surface roughness of Sa the substrate is 1.739 μm, and the error rate between simulation and experiment is only 0.6%. These results show that the macroscopic morphology of the substrate surface is uneven and laser scan trace, and the surface roughness Sa increases from 1.319 μm to 1.739 μm. It is found that there are passivation layer and bulge molten substance on the surface of the substrate, and the local area is smoother and even, and the chemical composition of the matrix has not changed. After laser paint removal, the microhardness of the substrate decreased from 141.59 HV to 130.37 HV, the corrosion rate decreased from 0.008 852 0 mm/a to 0.002 340 4 mm/a, and the microhardness of the substrate decreased and the corrosion resistance improved. Although the surface morphology and properties of the substrate are changed after laser paint removal, it has no effect on the reuse of the substrate and the repainting of the paint. This research provides a reference for improving the laser paint removal process, so as to reduce the matrix damage and obtain better paint removal effect.