The suspension design problem in electric vehicles is no longer a direct extension of that in conventional vehicles. In addition to conventional objectives such as ride comfort, road holding, suspension working space, and vehicle stability, electric vehicle suspensions must also deal with battery-induced mass redistribution, increased unsprung mass caused by in-wheel motors, electromechanical coupling effects, and the demand for energy-efficient chassis control. This paper reviews recent studies on the modeling, control, and optimization of electric vehicle suspension systems. First, representative suspension models are discussed, ranging from quarter-car and half-car models to full-vehicle and electromechanically coupled models for in-wheel-motor electric vehicles. Second, passive, semi-active, and active suspension control strategies are analyzed, including skyhook, groundhook, acceleration-driven damping, PID, LQR, H∞, sliding mode, fuzzy control, and model predictive control. Third, parameter optimization methods based on genetic algorithms, particle swarm optimization, artificial fish swarm algorithms, firefly algorithms, and multi-objective optimization are summarized. The review shows that current research is moving from isolated suspension design toward integrated chassis control, in which suspension dynamics, electric powertrain characteristics, ride comfort, road friendliness, and energy consumption are considered simultaneously. Finally, the main research gaps are identified, including limited full-vehicle experimental validation, insufficient consideration of real road conditions, and the need for robust multi-objective control strategies for electric vehicles operating under variable loads and road excitations.