<p>With the aggravation of energy problems and the promotion of sustainable manufacturing, the low-carbon concept has become increasingly prominent in workshop scheduling. As the saying goes, "When carbon is priced, the lowest-cost emission reduction measures will be implemented first." In this paper, the flexible job-shop scheduling problem with Type-2 Fuzzy Processing Time (T2FPT) is studied. At the same time, the maximum processing time, total carbon emissions, and machine load are considered. To address this problem, a Feedback Learning-based Evolution Algorithm (FLEA) was designed, incorporating five initialization strategies, four crossover and mutation operators. In addition, Q-learning is integrated as a feedback mechanism to dynamically adjust operator involvement during the evolutionary process, supported by a population state observation metric. Meanwhile interpolation method effectively reduces the carbon emission in the scheduling process. At the end of the algorithm, five neighborhood structures are designed for specific problems. Extensive experiments were conducted to evaluate the algorithm’s performance, and the results demonstrated that this method can effectively reduce the processing time and carbon emissions in the production process, showing great potential for workshop scheduling.</p>