In early phases of conceptual design stages for developing a new car in the modern automobile industry, the lack of systematic methodology to efficiently converge to an agreement between the aesthetics and aerodynamic performance tremendously increases budget and time. In this perspective, we have been committed to building an integrated and holistic decision-making environment to swiftly provide compromised solutions of external shapes of new cars by employing design-of-experiments (DOE) and surrogate modeling techniques that incorporate a series of procedures from creating geometries to conducting CFD simulations. As the first milestone, an automobile external shape reconstruction and optimization (AERO) algorithm which dynamically recreates shapes of various vehicles is developed. Once a 3-perspective schematic of a car is given, AERO algorithm regresses the backbone boundary lines by using appropriate polynomial interpolation methods, eventually reconstructing the 3-D shape by linearly interpolating from the extracted boundaries without losing the detail geometric features. The geometry data is fed into the automated CFD analysis processes by using ANSYS solutions. For validation and verification, several notional cars which are similar to Tesla Model S, INFINITI G37, etc. are explored by investigating aerodynamic performance. The drag coefficients (CD) are well matched with the reference data, demonstrating the shape modeling capability of AERO algorithm with outstanding quality and efficiency. Finally, an aerodynamic design optimization is performed as a preliminary case study for the design environment we aim to develop. Seeking to minimize the CD by manipulating four geometrical design variables, a significant amount of CD reduction is obtained.