New developments in road profile measurement systems and in semi-active damper technology promote the application of preview control strategies to vehicle suspension systems. This paper details a new semi-active suspension control approach in which a rule-optimized Fuzzy Logic controller is enhanced through preview capability. The proposed approach utilizes an optimization process for obtaining the optimum membership functions and the optimum rule-base of the preview enhanced Fuzzy Logic controller. The preview enhanced Fuzzy Logic controller uses the feedforward road input information and the feedback vehicle state information as the controller inputs. An eleven degree of freedom full vehicle model, which is validated through laboratory tests performed on a hydraulic four-poster shaker, is used for the controller synthesis. The cost function including both ride comfort and road holding performance of the full vehicle is minimized through a discrete optimization process utilizing Genetic Algorithm (GA). The preview distance is also considered as a design parameter during the optimization process. The performance of the preview enhanced rule-optimized Fuzzy Logic controller is evaluated by using a measured stochastic road profile as vehicle model input. The results demonstrate the potential of the preview enhanced controller in improving all aspects of system performance compared to the rule-optimized Fuzzy Logic controller without preview.