A new class of wind turbine termed the external axis wind turbine (EAWT) has been recently developed in response to the anticipated increase in global demand for renewable energy. The EAWT combines the high power output of the HAWT with the low installation and maintenance cost of the VAWT. This paper optimizes this EAWT to simultaneously maximize the power while minimizing power fluctuation and the time required to reach the optimal operating point. The multi-objective optimization on the EAWT using Genetic Algorithm is performed on a response surface that is generated using CFD simulations in OpenFOAM. The turbulence model and mesh-sizing for the CFD simulations are validated against previously published experimental results on a bluff body. The paper first optimizes the EAWT using steady state CFD simulations which are found to be not valid. The paper then optimizes the EAWT using transient CFD simulation with moving mesh, first for a single Reynolds number then for a wide range of Reynolds numbers. From the analysis, a blade count of 10 at an operational tip speed ratio of 032 is recommended. This paper is the first study on EAWT. To the best of authors' knowledge, this paper is also the first study on wind -turbines to optimize changing blade count and operating point to simultaneously maximize power, while minimizing power fluctuation and the time needed to reach peak operating point. (C) 2017 Published by Elsevier Ltd.