Abstract: In order to improve truck transport efficiency, reduce carbon emissions and save transport costs in open-pit mines, pure electric trucks are taken as the research object. The objective function is transportation cost, total queuing time (including truck charging time, operation time and maintenance waiting time in the production process), and ore grade deviation. The constraints include the crushing capacity of the crushing site, mining capacity of the mining site, loading capacity, ore grade error limit, vehicle charging pile selection and charging limit. The optimization model of low carbon transportation scheduling of open-pit is established. The gray wolf optimization (GWO) and non-dominated sorting genetic algorithm-II (NSGA-II) have been used to solve the low-carbon transportation scheduling model for pure electric mining trucks in open-pit mines. The former is prone to get trapped in local optimum while the latter is likely to achieve a global optimum but converges slowly. In order to solve the above problems, a GWO-NSGA-II hybrid algorithm is proposed. The hybrid algorithm introduces three genetic operations of NSGA-II, selection, crossover and mutation, into the GWO algorithm to effectively prevent the algorithm from falling into local optimum. In order to improve the stability of the global convergence of the algorithm, hunting and attack operations are introduced into the elite retention strategy of NSGA-II. Five standard test functions are used to verify that the hybrid algorithm improves the stability while ensuring the convergence. The example analysis shows that, compared with NSGA-II and GWO, the hybrid algorithm improves the optimization speed by 48.7% and 27.1% respectively. The hybrid algorithm improves the optimization precision by 17.1% and 9.3% respectively. The hybrid algorithm reduces the number of trucks, carbon emissions, transportation distance and transportation costs.