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In order to improve the efficiency of fire rescue, firefighters are increasingly using drones for fire situational awareness and monitoring. But drones are expensive. A hybrid drone equipped with a radio repeater or video and telemetry capabilities is expected to cost about $10,000. Therefore, in order to achieve the maximum economy and efficiency, I adopted the multi-objective programming model. The model mainly considers two objectives of economy and efficiency, and then sets constraint conditions to solve. Genetic algorithm and mathematical programming-based methods are the mainstream algorithms for solving Pareto frontier solutions in China [1]. NSGA-II algorithm is applied to solve the problem of UAV alignment. Taking the number combination coding of the decision variable UAV as the operational object, it can directly operate on structural objects such as sets, sequences, matrices, graphs [2]. On the one hand, this method is helpful to simulate the process of gene, chromosome and genetic evolution of organisms and facilitate the use of genetic operators. Reasonable and accurate UAV configuration scheme is given, which provides reference for the planning of relevant fire departments. On the other hand, genetic algorithm has a wide range of applications, such as function optimization, production scheduling and other fields.