With the rapid development of the new energy industry, how to deal with a large number of retired batteries is problem. The secondary utilization scenarios of retired batteries need to be determined based on the state of health (SOH). However, the traditional method of obtaining SOH is time-consuming and energy-consuming. Therefore, the study of fast SOH estimation is very meaningful. The unavailability of historical working condition information and the unknown state of charge at the time of detection make fast SOH estimation very difficult. For this reason, this article proposes a fast SOH acquisition strategy for retired batteries based on the difference in state of charges. In this article, the state of charge′s differences of different SOH retired batteries are used to generate multiple health features. Meanwhile, to select suitable hyperparameters for the random forest algorithm, the genetic optimization random forest regression algorithm is proposed to be applied for SOH estimation. Through experiments, the proposed strategy substantially reduces the estimation time of SOH for retired batteries. Through multiple strategies to avoid contact resistance and wire resistance during measurement, the error of health state estimation of 10 retired batteries is lower than 3% .