Vortex induced vibration (VIV) is a challenge in ocean engineering. Several devices including fairings have been designed to suppress VIV. However, how to optimize the design of suppression devices is still a problem to be solved. In this paper, an optimization design methodology is presented based on data-driven models and genetic algorithm (GA). Data-driven models are introduced to substitute complex physics-based equations. GA is used to rapidly search for the optimal suppression device from all possible solutions. Taking fairings as example, VIV response database for different fairings is established based on parameterized models in which model sections of fairings are controlled by several control points and Bezier curves. Then a data-driven model, which can predict the VIV response of fairings with different sections accurately and efficiently, is trained through BP neural network. Finally, a comprehensive optimization method and process is proposed based on GA and the data-driven model. The proposed method is demonstrated by its application to a case. It turns out that the proposed method can perform the optimization design of fairings effectively. VIV can be reduced obviously through the optimization design.