In order to overcome the problems of high computational complexity and long simulation cycle caused by the characteristics of strong dynamics, high timeliness, multiple constraints, and strong coupling during the three-dimensional spatial deployment of UAV-BS(unmanned aerial vehicle base station), an EGO(efficient global optimization) algorithm was proposed to determine the three-dimensional spatial deployment location of UAV-BS. Considering that the EGO algorithm mainly obtains new sampling points by optimizing the EI(expectation improvement) function, the improved DE(differential evolution) algorithm was proposed to optimize the EI function. The improved DE algorithm improves the optimization ability and convergence speed by adopting the successful parent selecting framework and the offspring generation strategy self-adaptive selection framework. Three typical engineering problems were selected to test the performance of the improved EGO algorithm. The results show that the optimization ability, optimization speed, and stability of the improved EGO algorithm are significantly improved. On this basis, an application example of using the improved EGO algorithm to deploy a UAV base station in three-dimensional space was given.