© 2020 Tech Science Press. All rights reserved. To improve the milling surface quality of the Al-Li alloy thin-wall workpieces and reduce the cutting energy consumption. Experimental research on the milling processing of AA2195 Al-Li alloy thin-wall workpieces based on Response Surface Methodology was carried out. The single factor and interaction of milling parameters on surface roughness and specific cutting energy were analyzed, and the multi-objective optimization model was constructed. The Multi-objective Particle Swarm Optimization algorithm introducing the Chaos Local Search algorithm and the adaptive inertial weight was applied to determine the optimal combination of milling parameters. It was observed that surface roughness was mainly influenced by feed per tooth, and specific cutting energy was negatively correlated with feed per tooth, radial cutting depth and axial cutting depth, while cutting speed has a non-significant influence on specific cutting energy. The optimal combination of milling parameters with different priorities was obtained. The experimental results showed that the maximum relative error of measured and predicted values was 8.05%, and the model had high reliability, which ensured the low surface roughness and cutting energy consumption. It was of great guiding significance for the success of Al-Li alloy thin-wall milling with a high precision and energy efficiency.