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• A novel simulation-based optimization framework for autonomous electric taxicab. • Incorporation of battery behaviors into the scheduling optimization problem. • Application of rolling horizon technique to capture the future ride information. • Break-down of the integrated problem to reduce the computational complexity. This paper proposes a simulation-based optimization framework for an autonomous electric taxi (AET) to achieve economic optimization by determining the optimal operations in the operating time horizon. The operating time horizon of the AET is equally divided into a set of consecutive time slots. For each time slot, there are four possible operations: driving, cruising, parking, and charging. To reduce the computational complexity, instead of solving the scheduling problem for the whole operating time horizon as a single problem, the whole problem is decomposed into a set of subproblems that are built for a one-day period. From an integrated electric vehicle simulation model, which simulates the AET operation based on the optimal schedule determined by the optimization problem, precise battery status parameters, such as the state of charge, capacity loss and battery temperature, are derived and used as the initial values for the optimization problem with rolling horizon implementation. A case study on NYC is presented, and the results show that the proposed framework can extend the battery life by 3%, and also increase the daily profit by 3% and 520%, compared to the 24hr rule-based strategy and 8hr rule-based strategy, respectively. [ABSTRACT FROM AUTHOR]