Unmanned aerial vehicle (UAV) design requires substantial effort in prototyping, testing, and iterations. To enhance design efficiency and performance, this thesis proposes an automated design and optimization framework utilizing open-source software, including OpenVSP, VSPAERO, the Parasite Drag Tool, XFOIL, and Python. The study begins with a preliminary design phase, estimating key parameters such as maximum takeoff weight, wing reference area, and engine power through statistical and constraint analysis. The wing design is optimized for aerodynamic performance, followed by the creation of a tail design for static stability. The fuselage, landing gear, and propulsion system are designed with a focus on motor and propeller selection, culminating in an initial baseline design model developed using the DegenGeom CAD module within OpenVSP. --- The framework addresses challenges in drag prediction, lift estimation, and airfoil selection, introducing a hybrid VLM-DATCOM model validated through computational fluid dynamics (CFD) and wind tunnel experiments. Its effectiveness is demonstrated through the design of two UAVs: a medium-sized thermal-engine UAV and a small electric-engine UAV. Key contributions include a systematic airfoil selection methodology, a robust drag correction model, and an optimization process that reduces design iteration time and costs. Accurate estimation of parameters like wing reference area, parasite drag and maximum lift coefficient is crucial for optimizing UAV design, impacting flight safety, endurance, stability, and overall performance. The results highlight improvements in aerodynamic efficiency and provide valuable insights for UAV designers in academia and industry.