The integrated scheduling problem has gained widespread application in the manufacturing and logistics industries due to its notable advantages in resource integration, cost control, and enhanced response speed. A Q-learning-based hyper-heuristic algorithm (QLHH) is proposed to solve the integrated scheduling of distributed production and delivery problem (ISDPDP) in this study. By deeply integrating Q-learning with the hyper-heuristic algorithm, the QLHH algorithm rapidly selects low-level heuristics using a Q-table and an improved greedy algorithm to explore the solution space. Energy-saving is fully considered in the production stage. The states and actions based on problem characteristics are designed to balance energy consumption with multi-objective scheduling and guide the global search. The QLHH accurately captures the core of the problem and addresses the multi-objective integrated scheduling challenge by designing low-level heuristics and energy-saving strategies based on problem knowledge. Experiments demonstrate that the QLHH algorithm outperforms comparison algorithms on test datasets, providing a practical solution for multi-objective integrated scheduling in distributed production and delivery problem.