In the event of a public health emergency, the selection of the optimal distribution locations of daily necessities is crucial. Accordingly, the emergency distribution of daily necessities in Changchun City was taken as an example for empirical research and an optimization algorithm was proposed for the location of emergency supplies distribution. Initially, the virtual material warehouses, distribution candidate locations, and service coverage areas were determined by using K-means clustering and the Thiessen polygon method. Then, an optimization model was established with the goals of maximum coverage and minimum cost, and an improved genetic algorithm based on adaptive elite retention strategy was proposed to solve it. Empirical research had shown that the clustering algorithm could obtain 873 virtual material warehouses and distribution candidates, while Thiessen polygons could determine the reasonable coverage range of candidates, and improved genetic algorithms could obtain better 30 material warehouses and distribution locations. Data analysis had also found that there was a time lag between the distribution of supplies and effective control of infection. Therefore, once the rate of new additions exceeds the normal value, emergency supplies should be increased in advance to control further outbreaks of possible infections. The research results are beneficial for enhancing the ability of urban crisis management and emergency response, ensuring timely and efficient supply of daily necessities.