Forced layout design in residential area is beneficial for increasing plot ratio, which is an important approach to achieve intensive construction. Existing residential forced layouts are mostly made by designers subjectively based on the results of sunshine simulation analysis, with low design efficiency. In the context of deep learning technology, a generative design method for residential forced layout based on conditional generative adversarial network (CGAN) was proposed, applying pix2pix algorithm to construct residential forced layout generative model. By learning the corresponding relationship between the outline and the general forced layout of low-rise, multi-story, and high-rise residential areas, the model could generate residential forced layout under any outline conditions, which improves the precision and efficiency of residential forced layout design and promotes the efficient use of urban land. Three residential areas in mid-latitude region were taken as examples to verify the application effect of the proposed method and evaluate the sunshine performance of the generated scheme. Results show that the generated low-rise scheme could meet the sunshine requirement of 2 h in Great Cold day (around January 20); 96% of rooms in multi-story scheme and 84% of rooms in high-rise scheme could meet the sunshine requirement. The plot ratio of the high-rise scheme was more than 3.0, that of the multi-story scheme was more than 1.5, and that of the low-rise scheme was more than 0.5, indicating that the generated schemes make effective use of urban land. The constructed model could generate residential forced layout within 3 s, which significantly reduces the design time of forced layout and improves the design efficiency.