Logistics project scheduling problem in indeterminate environment is gaining more and more attention in recent years. One effective way to cope with indeterminacy is to develop robust baseline schedule. There exist many related researches on building robust schedule in stochastic environment, where historical data is sufficient to learn probability distributions. However, when historical data is not enough, precise estimation on variables may be impossible. This kind of indeterminate environment can be described by uncertainty according to uncertainty theory. Related researches in uncertain environment are sparse. In this paper, our aim is to solve robust project scheduling in uncertain environment. The specific problem is to develop robust schedule with uncertain activity durations for logistics project. To solve the problem, an uncertain model is built and an intelligent algorithm based on simulated annealing is designed. Moreover, we consider a logistics project as a numerical example and illustrate the effectiveness of the proposed model and algorithm.