Abstract: In order to address the production scheduling problem of wind turbine extrusion plates (PSP-WTEP) with sequence-dependent adjustment time and sequential alignment constraints, a multi-objective optimization model is developed to minimize equipment load deviation, delivery time deviation and maximize equipment utilization. A modified multi-objective heuristic sparrow search algorithm (MHSSA) is designed based on the mechanisms of Pareto optimization and crowding distance calculation. A two-layer encoding strategy of "component-region" and a heuristic decoding operator of "inversion-repair-optimization" are incorporated in the algorithm. An improved population initialization strategy, which combines multiple rules with opposition-based learning, is adopted to enhance the global search capability of the algorithm. Additionally, an improved search strategy with crossover operators and an external archive disturbance mechanism is utilized to enhance the optimization accuracy and population diversity of the algorithm. The effectiveness of the proposed optimization model and intelligent scheduling algorithm is verified through dataset testing and instance simulation analysis.