In air defense and antimissile operations, constructing an efficient kill-net is a key part to ensuring the success of air defense tasks, while it is tricky to model and to optimize such problems. This paper aims to model the construction of static kill-net from the perspective of combinatorial optimization and propose efficient optimization methods. Considering the characteristics of the kill-net construction problem, this paper establishes a mixed-integer programming model and uses a bilevel optimization modeling scheme for simplification. By the cooperation of the leader problem of tasks assignment and the follower problem of resolving conflicts, the optimization difficulty is reduced. Subsequently, a solution framework based on a bilevel genetic algorithm is designed. In experimental tests on 4 sets of different-scale environments, the algorithm is able to get a fine result quickly, with great interpretability and a good capabilities for solving larger-scale problem. This work provides insights for the autonomous intelligent construction of static kill-net in the field of air defense and could serve as the basis for research focusing on dynamic kill-net adjustment.