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Distributed factory production is of great significance to improve the production efficiency of prefabricated components, ensure the on-time delivery of orders, and reduce the penalty cost of delayed delivery. Therefore, in order to minimize the penalty of total order delay, a mathematical optimization model is established for the scheduling problem of distributed prefabricated component flow shop, and a discrete teaching-learning based optimization (DTLBO) is proposed based on double-layer integer coding. In the initial stage of the algorithm, heuristic rules and random generation fusion strategy are used to improve the quality of the initial solution, so as to increase the optimization efficiency of the algorithm; in the teaching stage, combined with the characteristics of the problem model, two kinds of neighborhood structures: top-level replacement and bottom-level replacement, are designed to promote the guidance and optimization of the teacher's solution to the student's solution; in the learning stage, the mutation operator and crossover operator are used to let students learn and update each other, so as to further improve the local development and global exploration ability of the algorithm. Experimental results show that compared with genetic algorithm and variable neighborhood search algorithm, the proposed DTLBO achieves better solution quality and robustness. Finally, compared with the empirical heuristic scheduling method commonly used in the actual production process, the proposed algorithm shows an average improvement rate of no less than 10% on the target value, which is expected to significantly increase the net profit of the prefabricated component manufacturing enterprise and improve customer satisfaction, and can provide a better and reasonable production scheduling scheme for enterprise managers.