Minimum energy consumption with maximum comfort driving experience define the ideal human mobility. Recent technological advances in most Advanced Driver Assistance Systems (ADAS) on electric vehicles not only present a significant opportunity for automated eco-driving but also enhance the safety and comfort level. Understanding driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system comfort. This research focuses on the personalized, green and adaptive cruise control for intelligent electric vehicle based on the optimization of regenerative braking and typical driving styles, also known to be MyEco-ACC. First, a driving style model is abstracted as a Hammerstein model and its key parameters vary with different driving styles. Secondly, the regenerative braking system characteristics for the electric vehicle equipped with 4-wheel hub motors are analyzed and braking force distribution strategy is designed. Finally, MyEco-ACC is constructed and optimized via theory of Model Prediction Control. Regenerated energy is taken as the indicator for energy consumption and the key parameters in driving style model are taken as the comfort indicator. The strategy is simulated on a real-time Driver-In-the-Loop Intelligent Simulation Platform with Mathwork Simulink® , PanoSim-RT®, dSPACE® and DEWETRON®. Test results show that driving styles can be classified clearly and identified effectively and the driving style model has a high fidelity. Furthermore, simulation results show that the proposed personalized eco-driving strategy can satisfy the requirements of personalized driving styles and has a significant improvement in energy-recovery-rate promotion compared with non-optimized strategy in the same simulation conditions.