###### Abstract
[21] performed an optimization design between multiple fields at the primary stage, applied genetic algorithms to optimization, and used gradient methods to non-planar shapes to compare and analyze efficiency. [PERSON] et al. [22] performed optimization to find an accurate solution to the aircraft's performance and structure and designed using PBLI, a genetic algorithm. [PERSON] et al. [23] investigated various stacked structures that were insufficient in existing optimization techniques by applying genetic algorithms to rigid and weight optimization designs. [PERSON] et al. [24] demonstrated that the improvement of genetic algorithms and the optimization of applying genetic algorithms to aerodynamic forms of 2-D/3-D wings are similar to traditional design methods. [PERSON] et al. [25] presented a genetic algorithm for adjusting optimal PID parameters in DTC to control DFIM using objective functions such as integral square error, integral time absolute error (ITAE), and integral absolute error. [PERSON] et al. [26] proposed a formal model of constraints and questions about extended vehicle routing problems for drones, including a dynamic selection of drone take-off points, two-way delivery (delivery and pickup), various types of delivery, delivery allocation to drones, drone allocation to vehicles, and optimal drone number selection. [PERSON] et al. [27] described a simulation optimization methodology that combines simulation, genetic algorithms, and new solution representations to design an integrated plan for container relocation in a port yard and a storage plan problem to unload ships through the port. Finally, [PERSON] et al. [28] used genetic algorithmsand discrete particle cluster optimization algorithms to manage the complexity of the problem, calculate feasible and quasi-optimal trajectories for mobile sensors, and determine the demand for movement between nodes. [PERSON] et al. [29] described parametric modeling and design sensitivity analyses performed on hat stiffener elements for single- and multiple-hat-stiffened panels using parametrically defined scripting finite element analysis (FEA) models and an idealized analytical solution. [PERSON] et al. [30] optimized a UAV wing structure that carries more than 100 times its weight and was developed and tested to validate the design approach and demonstrate load carrying ability and manufacturing quality. [PERSON] et al. [31] conducted non-linear aeroelastic analyses using multi-fidelity tools to assess whether geometric non-linearities affected the optimized designs. The wing structure of the reconnaissance UAV is a thin and long shape, as it increases the duration of flying time. To maximize the efficiency of mission performance, the duration of flight in the mission area should be much longer than that of manned aircraft. Reducing drag and improving productivity should also be considered a shape suitable for surveillance reconnaissance and long-term flight. However, all shapes must be applied, and the fabrication process of the structure requires considerable manufacturing know-how, and it is difficult to ensure the reliability of the structure. In addition, interference between the outer skin and the inner structure should be overcome, and a detailed optimal stacking design is required for each wing part. This paper uses genetic algorithms to consider the characteristics of thin and high aspect ratio wing structures and to achieve mass optimization. It provides a framework for a method for finding optimal stiffness or laminate sequence distribution. Through genetic algorithms, design variables are calculated to quickly satisfy the dominant design conditions of composite wing structures and strength and stability conditions through variable lamination. The stiffness change should be performed directly when changing the fiber angle or thickness within the part.