Reusable spacecraft has great potential in reducing space launch cost. Structural reliability evaluation is critical for mission planning of reusable spacecraft. A dynamic reliability prognosis method based on digital twin framework is proposed for mission planning in the paper. In this method, Uncertainties integration and dynamic model updating are implemented through a dynamic Bayesian network. A maintenance point is set when the predicted structural reliability level is lower than a threshold or unexpected conditions such as landing impact occur. Then, inspected data can be assimilated by the framework to dynamically update the structural reliability. Thus, it supports dynamic adjustment of maintenance interval, early warning of structure failure, and mission planning with quantified risk. A numerical example considering single point crack growth under fatigue load and landing impact of a simplified spacecraft structure is used for demonstration. Results show that the crack size predictions can be calibrated by inspected data and its uncertainties can be reduced. The proper selection of landing impact probability in reliability prediction is helpful to control the maintenance interval. The reliability of the spacecraft can be increased through model updating with new inspected data, representing a potential lifetime extension can be realized by the proposed method.