Current study presents fluid flow analysis using CFD and a surrogate based framework for design optimization of Savonius wind turbines. The CFD model used for the study is validated with results from a physical model in water tunnel experiment. Four variables that best define blade geometry are considered and a feasible design space consisting of different combinations of these variables that provide positive overlap ratio is identified. The feasible space is then sampled with Latin hyper cube design of experiment. Numerical simulations utilizing K-epsilon turbulence model are performed at each point in the Design of Experiments to obtain coefficient of performance and weighted average surrogate (WAS) is fitted to them. Novelty of the current work is the use of WAS for design of savonius turbine. The WAS is an ensemble of surrogates that consists of polynomial response surface, kriging and radial basis functions. Error metrics reveal that WAS performs better compared to any surrogate individually thus avoiding misleading optima and eliminates surrogate dependent optima. WAS is used to explore the design space and perform optimization with limited number of CFD analyses. It is observed that at the optimal profile, there is more power on the rotors and primary recirculation in the immediate downstream of rotor is high, enforcing maximum momentum on turbine. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.