The demand response of Home Energy Systems (HES) contributes to improving human life and promoting energy conservation and emission reduction. Due to the uncertainty of HES and the diverse living habits of people, demand response optimization strategies need to have the ability to quickly adapt and satisfy people's preferences for using different home appliances. Therefore, this paper proposes an intelligent optimization algorithm that integrates Human Preference Reinforcement Learning (DRLHP) with evolutionary computation. The algorithm first uses collected preference information to train a reward generator based on human preferences. Then, the reward generator replaces the reward function of traditional deep reinforcement learning algorithms and interacts with the HES model, continuously learning the complex patterns of the model. Finally, to further enhance the personalization capability of the algorithm, evolutionary computation is employed based on DRLHP to further optimize the scheduling plan. The case study demonstrates that the proposed intelligent optimization algorithm has fast solving speed, strong optimization ability, and robustness, achieving energy conservation and emission reduction while satisfying human preference needs.