Abstract: To enhance decision−making efficiency and reliability for multi−stope and multi−equipment production scheduling in underground metal mines, this study addressed the challenges of complex mining processes, limited production resources, and high−volume production tasks. A multi−objective process scheduling optimization model was developed for underground mines, aiming to maximize net present value (NPV), minimize total Makespan (completion time of all processes), and minimize schedule robustness. The model utilized a time window strategy to determine the optimal start times for mining processes, enabling efficient and economical extraction alongside scientifically effective equipment scheduling. A hyper−heuristic algorithm based on a Ranked Selection Function (RSF) was designed to solve the multi−objective optimization model. The RSF, serving as the high−level selection strategy, employed multi−objective heuristic indicators to guide the choice of low−level metaheuristic operators, namely NSGA−II, MOEA/D, and MOPSO. Model validation was conducted using a case study from an underground tin−copper mine in Guangxi, China. This mine primarily utilized the sublevel open stoping with subsequent backfill mining method. Experimental results show that the proposed model and algorithm can generate optimal process and equipment scheduling schemes for the underground metal mine, ensuring economic benefits throughout the mine's production lifecycle.