Aiming at the allocation of teachers in engineering Massive Open Online Courses (MOOCs), this study constructs a multi-objective optimization model that comprehensively considers teaching quality, workload balance, and course coverage. The model clearly defines the allocation relationship between teachers, course modules, and time periods through mathematical expressions and designs key indicators such as professional matching and time constraints. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) are used for solution, and simulation experiments verify the effectiveness of the model in various scenarios. The results show that NSGA-II performs well in terms of teaching quality, while MOEA/D has more advantages in balance and efficiency. Visual analysis, such as the Pareto frontier and allocation heat map, intuitively demonstrates the diversity and feasibility of optimization schemes, providing a scientific teacher management tool for MOOC platforms with strong application value.