SSP-MMC is a strategy for memorizing English words based on optimizing spaced repetition, it aims to minimize the memory cost while ensuring the memory effect (such as the target half-life). However, SSP-MMC still has a high memory cost. To address this issues, we propose a new interval repetition algorithm based on dynamic adjustment of users’ reaction time called SSP-MMC++. We think that reaction time is also an important parameter and reflects the fluency of memory retrieval. Specifically, we first add the reaction time variable to the memory model to improve the state transition equation. Then we add the reaction time variable to the Bellman equation to find the optimal review strategy. Experiments show that, compared with the original SSP-MMC algorithm, the new method SSP-MMC++ improves the target half-life achievement rate by 22.8%, reduces the cost of memory retention by 16.8%, and increases the total amount of learning by 23.8%.