Machine tools play a vital role in the manufacturing industry and their wide application leads to high energy consumption. Considering energy saving and emission reduction, it becomes particularly important to reduce the energy consumption of machine tools while improving processing efficiency. Selecting appropriate milling parameters while optimizing the processing power and efficiency of the machine tools is a challenge. In this paper, an adaptive multi-objective differential evolutionary PSO based on analytic hierarchy process (AMDEPSO) is proposed to optimize the two objectives. Firstly, an accurate multi-objective optimization model integrating the processing power and time of machine tools is established. In order to solve this optimization model, this study sets adaptive weights based on PSO and incorporates the differential evolution (DE) method to update particles in the local search. Subsequently, the Pareto solution set is filtered to obtain the optimal solution through analytic hierarchy process (AHP). Finally, the algorithm is verified by processing experiments, and compared with the two existing methods, the proposed method effectively reduces the processing power and improves the processing efficiency. Combining the relationship between the two optimization objectives, it can be obtained that the algorithm reduces the machining energy consumption by 45.58% and 47.15% respectively.