<p>This study addresses the truck-shovel joint assignment problem in open pit mines through a multi-objective optimization framework, aiming to minimize operational costs while maximizing transportation volume. A mathematical model is proposed that incorporates practical constraints, including mining limits, fleet capacities, ore grade tolerances, and unloading restrictions. Based on the characteristics of the model, a constraint repair mechanism tailored for the truck-shovel joint assignment problem is designed and an improved multi-objective optimization algorithm is proposed, which incorporates the repair mechanism, a hybrid encoding scheme, and customized evolutionary operators. The mechanism ensures the feasibility of the solution by exploiting the relevance between the constraints and objectives, achieving synchronized improvements in feasibility and performance. Numerical experiments in various mining scenarios demonstrate that the proposed algorithm delivers constraint-compliant Pareto optimal solutions with superior convergence and diversity compared to state-of-the-art methods. Using the repair mechanism, the proposed algorithm significantly reduces costs and eliminates restrictions violations, providing robust and practical decision-making support for sustainable mining operations.</p>