The traditional interior layout design method has the problem that it is difficult to express the optimization goal clearly, and the design process lacks flexibility and individualization performance. To solve the above problems, interactive differential evolution algorithm and reverse learning strategy are used to optimize interior layout design, so as to better meet users’ individual needs for interior layout design. The study first analyzes the indoor layout design based on improved interactive differential evolution algorithm, then analyzes the application of interactive differential evolution indoor layout with reverse learning strategy, and finally analyzes the performance and application results of the interactive differential algorithm with reverse learning strategy. It was verified that the algorithm had the fastest running time in the unimodal function F1, which was about 14 seconds. In addition, the algorithm could find the global optimum in the four benchmark functions F1, F5, F7, and F10. For European style space design, the research model had a high level of user satisfaction, with the highest satisfaction value reaching 88 in the later stages of iteration. For minimalist style space design, the research model had a high level of user satisfaction, with the highest satisfaction value reaching 98 in the later stages of iteration . The study demonstrates that the integration of an interactive differential evolution algorithm and reverse learning strategy enhances the flexibility and personalization of interior layout design. This approach substantiates the research method’s potential and advantages in the domain of interior space layout design, offering novel insights and methods for future research.