Genetic algorithms (GA) are being widely used as an evolutionary optimization technique for solving optimization problems involving non-differentiable objectives and constraints, large dimensional, multi-modal, overly constrained feasible space and plagued with uncertainties and noise. However, to solve different kinds of optimization problems, no single GA works the best and there is a need for customizing a GA by using problem heuristics to solve a specific problem. For the airborne flight planning problem, there is not much prior optimization studies made using any optimization procedure including a GA. In this paper, we make an attempt to devise a customized GA for solving the particular problem to arrive at a reasonably good solution. A step-by-step procedure of the proposed GA is presented and every step of the procedure is explained. Both single and multi-objective versions of the problem are solved for a particular scenario of the flight planning for airborne LiDAR data acquisition problem to demonstrate the use of a GA for such a real-world problem. The deductive approach successfully identifies the appropriate configurations of GA. The paper demonstrates how a systematic procedure of developing a customized optimization procedure for solving a real-world problem involving mixed variables can be devised using an evolutionary optimization procedure.