Aiming at the problems of poor scheduling scheme，low utilization rate and high cost of unmanned trucks in large surface coal mines，an optimization method based on digital twin simulation is proposed，a digital twin system framework for unmanned mining trucks is constructed，and a digital twin simulation model is built with the help of AnyLogic software to realize real-time mapping between physical and virtual spaces. For the multi-objective and multi-constraint unmanned truck scheduling optimization problem，a mathematical model with the objectives of minimizing the transportation cost，minimizing the total transportation time，and maximizing the truck utilization is established，and the Quantum Enhanced Particle Swarm Optimization Algorithm (QIPSO) is designed to solve the problem. Validated by an example of a surface coal mine in Xinjiang，the results show that compared with the traditional particle swarm algorithm，improved particle swarm algorithm and genetic algorithm，the QIPSO algorithm reduces the maximum completion time，total transportation cost by 11.34% and 10.62% respectively，while increasing truck utilization by 9.14%，and has a faster convergence speed and better stability. The digital twin scheduling scheme shortens the task completion time by 73 minutes compared with the traditional scheme，effectively improves the transportation efficiency，and provides a feasible technical path and methodological support for the intelligent scheduling of unmanned trucks in surface coal mines.