Retina-like sensors maximize both field of view and resolution in addition to economizing on pixel count, so they play an important role in both biological and machine vision. A new retina-like sensor model for compensating motion blur introduced by forward motion imaging is proposed. Next, the determination of pixel arrangement of a retina-like sensor according to visual task requirements is formulated into a multiobjective optimization problem. Then, three retina-like sensors are designed to meet different visual task requirements using the particle swarm optimization algorithm. The results are robust and approximate to design criteria.