In practical production processes, equipment performance gradually degrades over time, leading to extended processing durations. To address this issue, an improved nondominated sorting genetic algorithm II (NSGA-II) was proposed for a re-entrant hybrid flow-shop scheduling problem that considers deterioration effects. Firstly, a mathematical model was formulated with the optimization objectives of minimizing makespan and reducing processing energy consumption. Secondly, a job-sequence-based encoding method was employed, and an energy-efficient scheduling decoding method that accounts for deterioration effects was designed. Additionally, to enhance population diversity, various mutation operators were introduced, and algorithm parameters were adaptively adjusted to prevent convergence to local optima. A variable neighborhood search strategy was also integrated to reinforce the local search capability of the algorithm. Finally, comparative experiments with other algorithms on ten different scale test instances demonstrated that the proposed algorithm delivers superior solution quality, along with better diversity and convergence properties.