In recent years, airlines have increasingly developed the ability to monitor the condition of aircraft components by means of sensors. In turn, aircraft maintenance aims to use this sensor data to predict component failures. However, the challenge remains to make use of these prognostics to generate appropriate maintenance schedules. In this paper, we develop a Monte-Carlo tree search to schedule maintenance tasks based on component prognostics and available maintenance slots. This approach is used to create a maintenance policy for multiple aircraft which specifies which aircraft are allocated for maintenance and on which days. The results show that the scheduling of the maintenance tasks is robust and able to accommodate the maintenance scheduling of smaller airline fleet sizes. Overall, our results support the integration of aircraft component prognostics in aircraft maintenance scheduling.