Abstarct: Reducing transportation costs and increasing productivity in loading and hauling operations of open-pit mines have become major challenges in mining engineering. Designing an effective scheduling plan to determine the optimal number and locations of loading points can play a significant role in cost management and production enhancement. The aim of this study is to present a multi-objective model for the integrated optimization of these factors within a practical and scientific frxamework. The research methodology involved the development of a multi-objective mixed-integer linear programming (MILGP) model, incorporating multiple operational goals, and solved using a weighted coefficients approach within the CPLEX environment. The model results indicated that the desired tonnage at discharge destinations, according to the target ore grade, stripping ratio, and oxide-to-total copper ratio, all remained within the acceptable ranges. For model validation and optimal truck fleet determination, discrete event simulation (DES) was performed using ARENA software across 11 scenarios involving both homogeneous and heterogeneous fleets. Key operational metrics such as waiting times, loading/unloading durations, and truck round-trip times were evaluated. Ultimately, after scenario scoring, scenario 11—with a fleet of 33 type-1 trucks and one type-2 truck—was identified as the optimal solution. The research methodology involved the development of a multi-objective Mixed-Integer Linear Goal Programming (MILGP) model, which was solved using weighted coefficients in the CPLEX environment. The model results indicated that the targeted tonnage at dumping destinations—baxsed on ore grade, stripping ratio, and the ratio of oxide to total copper—fell within the desired operational range. To validate the model and determine the optimal number of trucks, Discrete Event Simulation (DES) was conducted using ARENA software. Under 11 homogeneous and heterogeneous scenarios, key operational indicators such as waiting time, loading and unloading durations, and truck cycle times were analyzed. The final results demonstrate that the proposed integrated approach, baxsed on mathematical optimization and simulation, enables precise decision-making regarding resource allocation, determining the optimal number of trucks, and selecting the most efficient loading points in open-pit mines. The proposed frxamework of this study can serve as a practical model for designing loading and hauling systems in similar mining operations and can play a significant role in improving operational performance and reducing costs in the mining industry.