In this paper, an optimal preview control algorithm applied to a quarter car vehicle model with semi-active magnetorheological (MR) damper traversing a random rough road with constant velocity is considered. The weighting factors of the controller are determined by a new approach using a multi objective optimization technique using the non-dominated sorting genetic algorithm (NSGA II) in combination with a pareto optimal scheme with a constraint on the mean square control force to lie between the control force of the MR damper for zero and maximum input currents. The MR damper used in the study corresponds to an actual twin tube MR damper, indigenously developed in the laboratory. The experimental MR damper behaviour like force-time, force-displacement and force-velocity are portrayed by a modified Bouc-Wen model and whose parameters are estimated by a similar multi objective optimization procedure using NSGA II algorithm. Results are presented in terms of sprung mass acceleration, suspension stroke, road holding, control force and overall performance for a quarter car vehicle model with passive, H∞ active controller with preview and semiactive MR damper with designed preview controller using H∞ estimator for different preview distances. The results show that the performance of the semi-active MR damper with preview control increases with increase in preview distance and saturates beyond a preview distance. The performance of semi-active MR damper with preview control is much better than that of passive suspension for different preview distances and approaches H ∞ active suspension with preview. © Civil-Comp Press, 2008.