To address the green parallel Batch Processing Machine （BPM） scheduling problem with redyeing operations in textile factory dyeing workshops， a Multi-level Teaching-Learning-Based Optimization （MTLBO） algorithm was proposed to minimize makespan， total energy consumption， and total weighted advance/delay cost. Firstly， heuristic rules were employed to generate the initial population for improving the initial solution quality. Secondly， the population was divided into three layers — teacher group， elite class， and ordinary class through multi-level structure， with an inter-layer efficient communication mechanism designed for information sharing and knowledge inheritance. Finally， to enhance exploration ability of the population， and to avoid the algorithm from the local optimum， a diversity enhancement operator based on probability model was introduced to replace stagnant solutions. Test instances generated on the basis of industrial data were used to evaluate MTLBO’s performance， and it was compared with the algorithms such as Adaptive Shuffled Frog-Leaping Algorithm （ASFLA）， Multi- Objective Artificial Bee Colony （MOABC） algorithm， Fuzzy Genetic Algorithm （FGA）， and Non-dominated Sorting Genetic Algorithm-Ⅱ （NSGA-Ⅱ）. The experimental results indicate that on average， the MTLBO has the dominance relation of non-dominated solution set 81.92% higher， the coverage metric 97.58% lower， and the convergence metric 99.66% lower. The above verifies MTLBO’s superior exploration ability and stability in optimizing scheduling metrics， providing robust solutions with optimization efficiency for practical production decision-making.