Distributed scheduling is the frontier research in the field of manufacturing systems, while the study on the uncertain scheduling problems is of more realistic significance. For the uncertain distributed permutation flowshop scheduling problem, interval processing times are used and the minimization of interval maximum completion time is considered as the objective. To solve the problem, a hybrid discrete fruit fly optimization algorithm is proposed with the framework of the fruit fly optimization by using the characteristics of the problem. Firstly, an improved heuristic and the random method are used in a hybrid way to initialize the population. Then, probabilistic cooperation of multiple search operators is executed as the olfactory search. To balance the exploration and the exploitation capabilities, a learning-based bi-population cooperation scheme is designed. Meanwhile, local intensification search based on a bi-mode switch scheme is applied for the elite solutions for further improvement. Simulation results and statistical comparison with extensive instances show that the proposed algorithm can solve the distributed flowshop scheduling with interval data more effectively.