Battery optimization is a challenging task due to the large amount of cost and time required to evaluate different configurations in experiment or simulation. It is especially costly to optimize cycling performance since cycling the batteries is costly and time-consuming. In this project, we report an optimization framework with learned sampling and early stopping strategies, designed to optimize battery parameters in an efficient way to reduce total cycling time. It consists of a pruner and a sampler. The pruner stops unpromising cycling batteries to save the budget for further exploration. The sampler can predict the next promising configurations based on query history. The framework can deal with categorical, discrete and continuous parameters, and can run in an asynchronously parallel way to allow multiple simultaneous cycling cells. We demonstrated the performance by a parameter fitting problem for a calendar aging model. Comparisons with current methods are made to demonstrate the effectiveness of our method to reduce cycling time. Our framework can foster battery optimization in simulations and experiments.