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Selecting a set of reasonable milling parameters of computerized numerical control (CNC) machines is of great importance in decreasing energy consumption and enhancing processing productivity. However, existing works pay little attention to the optimization of energy-conscious milling parameters. This work establishes a dual-objective optimization model for the selection of milling parameters such that power consumption and process time are minimized. With multiple constraints of milling processing conditions, an improved artificial bee colony (ABC) intelligent algorithm is used to handle the proposed dual-objective optimization model. Compared with the non-dominated sorting genetic algorithm (NSGA-II), our improved algorithm has good performance. [ABSTRACT FROM AUTHOR]