Double-skin façades (DSFs) are high-efficiency systems traditionally used to improve the natural ventilation of buildings for energy-saving purposes and their aesthetic improvement. There are opportunities to enhance their functionality by applying them to improve building aerodynamics. This study aims at developing the preliminar data set that would assist with design of smart morphing facades (also known as Smorphacades). For this purpose the a DSF for rectangular and elliptical tall buildings is studied with the final goal of minimizing the aerodynamic loads on such structures. For this purpose, an integrated framework including numerical modeling and statistical analysis was developed. The design of experiment (DOE) method was applied to produce a sufficient dataset based on computational fluid dynamics (CFD) simulation of two-dimensional (2D) scaled models that were validated with available wind tunnel data. According to the data points recommended by the DOE method, a considerable number of CFD simulations were performed, and the drag coefficients of the building and double-skin façade were separately calculated. A prediction model based on the response surface methodology (RSM) was developed to estimate the drag coefficient for other cases inside the design space. Based on the fitted RSM, the Genetic algorithm was applied to search for the optimized Smorphacade shapes. The results indicated that the integrated smorphacade system could significantly mitigate wind-induced drag forces on the building at all attack angles by modifying wind-induced pressure around the building and weakening vortex shedding by interrupting flow separation and ejecting airflow into a lower-pressure area. This research proves that there are opportunitie to integarte the architectural and energy applications of smart double skin facades with wind-reducing effects as a promising solution for overcoming existing challenges for controlling wind-induced load and response of tall buildings.