Abstract: Open-pit coal mining is a complex system involving multiple intersecting processes, whose three-dimensional dynamic spatiotemporal evolution requires scientific planning. However, due to the lack of a temporal sequencing planning method, mining plans still rely on manual preparation, resulting in crude planning and frequent adjustments to production decisions. To establish an optimal material extraction sequence plan within the closed spatiotemporal field of open-pit mines, this study analyzes the impact of flow direction and flow rate planning of coal and rock block models on mining plan optimization. By incorporating transportation costs into the economic value attributes of block models and aiming to maximize total net present value (NPV), a mixed-integer programming (MIP) optimization model is developed, considering various complex constraints. To address the limitations of the traditional GWO algorithm in terms of convergence and local extremum avoidance when solving the MIP optimization model, an improved GWO algorithm is proposed. The enhanced algorithm integrates circle chaotic mapping to increase population diversity, introduces a nonlinear update mechanism for parameter adjustment, and employs an individual information-sharing strategy to enhance global search capabilities. Simulation experiments verify the correctness of the proposed model and algorithm. Using the Baorixile open-pit coal mine as a case study, the material flow direction and flow rate for block models from 2024 to 2028 are planned, and the corresponding mining plan is developed. The results demonstrate that the optimized plan increases total NPV by 5.27% and improves efficiency by 19 times compared to traditional manually prepared plans. The developed planning model and improved algorithm significantly enhance the scientific rigor and economic efficiency of open-pit mining plans, providing theoretical support for automated decision-making in open-pit coal mining.