Aiming at the path planning challenge for long-distance transfers of stratospheric airships, a time-optimal path planning method based on an improved rapidly-exploring random tree star (RRT*) algorithm (T-RRT*) was proposed, it is necessary to comprehensively consider complex wind fields, the weak dynamic characteristics of the platform, and the feasibility constraints of flight missions. Firstly, the stratospheric environment as well as the kinematics of stratospheric airships were modeled, and a two-dimensional obstacle environment model was established by combining the constraints of stratospheric airships. Secondly, the influence of dynamic wind field was considered, and the time cost function of T-RRT* algorithm was designed through the idea of superposition of Euclidean distance and velocity vectors, which was used to cope with the complex wind field and the dynamic constraints of the airships. Finally, path planning simulation analyses were conducted across multiple scenarios using environmental data such as cold cloud and wind field data. Results show that compared with the traditional rapidly-exploring random tree (RRT) and the RRT* Algorithm based on path Length optimization(length-optimal RRT* algorithm, L-RRT*) path planning, the average time consumed by the paths planned by the T-RRT* algorithm in the real dynamic wind field environment can be shortened by 28.7% and 8.7%, respectively, which is conducive to the optimization of the energy consumption of the transfer paths and the enhancement of the mission adaptability, and it provides a new technological idea for the path planning of the stratospheric airships.