With the commercialization of the electric vehicles, the large-scale lithium-ion cells as the power of electric vehicles are to be retired. The second-use of retired cells is of great significance to improve the battery economy. A fast classification and regroup evaluation method of the retired lithium-ion cells are proposed in this paper to improve the classification efficiency of retired lithium-ion cells and adapt to the regroup under different conditions. The lithium-ion cells after being balanced in parallel are charged in series with a constant current. A support vector regression (SVR) model with the parameters optimized by the particle swarm optimization (PSO) algorithm is built for the fast capacity estimation and the error will not exceed 0.3%. Different cells regrouped means different performance. In order to improve the consistency of retired cells and satisfy different using conditions, a Weighted-K-means algorithm is proposed in this paper to regroup the cells with the known capacity and internal resistance. The classification method is evaluated by the voltage consistency of cells using different working conditions, which indicates capacity occupied a large proportion can meet the requirement of energy condition meanwhile keep a good consistency. But the resistance will dominate in algorithm under conditions which have requirement for instantaneous power.