The work probes into the model design of Low-Level Transit Route (LLTR) planning in air defense operation scenarios and its related optimization algorithm. In the model design, route shortcut, radial velocity, and relationship of routes are taken as objective functions, while air defense restriction, fighter performance, route range and route coordination are regarded as constraints. In the algorithm implementation, an Improved LangEvin Equation based Evolutionary Algorithm (ILEE) is proposed. The algorithm is improved via Tent map-based chaotic initialization, hybrid dynamic perturbation fused with Lévy flight and Cauchy mutation, and dynamic boundary elite opposition-based learning. Simulation experiments show that the proposed method can generate reasonable LLTR based on air defense operation requirements regardless of the number and deployment of surface-to-air missile positions. Meanwhile, compared with existing swarm intelligence algorithms, the improved algorithm exhibits superior performance in test functions, which can provide a reference for air defense operation airspace planning.