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Business process simulation is a versatile technique for analyzing business processes from a quantitative perspective. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, several authors have proposed to discover simulation models from process execution logs so that the resulting simulation models more closely match reality. Existing techniques in this field assume that each resource in the process performs one task at a time. In reality, however, resources may engage in multitasking behavior. Traditional simulation approaches do not handle multitasking. Instead, they rely on a resource allocation approach wherein a task instance is only assigned to a resource when the resource is free. This inability to handle multitasking leads to an overestimation of execution times. This paper proposes an approach to discover multitasking in business process execution logs and to generate a simulation model that takes into account the discovered multitasking behavior. The key idea is to adjust the processing times of tasks in such a way that executing the multitasked tasks sequentially with the adjusted times is equivalent to executing them concurrently with the original processing times. The proposed approach is evaluated using a real-life dataset and synthetic datasets with different levels of multitasking. The results show that, in the presence of multitasking, the approach improves the accuracy of simulation models discovered from execution logs.