Production scheduling of underground metal mines is confronted with severe challenges caused by multi-source uncertainties, such as geological conditions, equipment status, and safety risks, while traditional deterministic scheduling models struggle to guarantee system stability and operational efficiency. To address this issue, a Monte Carlo simulation-based scheduling optimization model(MC-MOO) for underground metal mines was proposed. Firstly, probability distribution models were established for multi-source uncertainty factors. Subsequently, Monte Carlo scenario simulation was integrated with the NSGA-Ⅱ multi-objective optimization algorithm to develop a closed-loop framework of "probabilistic modeling-scenario simulation-multi-objective optimization", enabling the comprehensive optimization of scheduling cost, production stability, and equipment load balancing. The case study results show that compared with the traditional deterministic scheduling models, the total scheduling cost of the MC-MOO model is reduced by 5.97%, the average monthly output is increased by 16.53%, the standard deviation of output is reduced by 41.82%, and the equipment load balance is increased by 23.61%. It provides an effective modeling and solving paradigm for mine intelligent scheduling under uncertain environment.