The invention discloses a cost optimization unmanned aerial vehicle base station deployment method based on an improved genetic algorithm, and mainly solves the problem that the unmanned aerial vehicle base station deployment cost is difficult to optimize in the prior art. The realization method comprises the following steps: 1) establishing a ground wireless communication coverage model of the unmanned aerial vehicle base station; 2) calculating the maximum coverage radius and the optimal hovering height of the unmanned aerial vehicle base station in the unmanned aerial vehicle base station ground wireless communication coverage model scene; 3) deploying the unmanned aerial vehicle base stations at the optimal hovering height, enabling the deployment problem to be reduced from three-dimensional dimensionality to a two-dimensional plane, establishing an unmanned aerial vehicle base station deployment optimization model taking unmanned aerial vehicle base station deployment number optimization as a target, and solving the model to obtain an optimal chromosome; 4) converting the optimal chromosome into a corresponding unmanned aerial vehicle base station coordinate set to obtain an optimal unmanned aerial vehicle base station deployment scheme, reducing the complexity of the deployment problem, improving the solution accuracy, and being applicable to communication network deployment planning, temporary communication network construction and disaster area emergency communication.