For the uncertain multi-objective flexible job shop scheduling problem, the processing time is presented by interval number. A multi-objective interval flexible job shop scheduling problem model is established, and an effective multi-objective evolutionary algorithm(MOEA) is proposed to minimize interval makespan and interval total workload. Firstly, a population is initialized by adopting the hybrid strategy, and a greedy insertion method is designed for chromosome decoding. Then, an interval dominance relationship based on the possibility degree is employed to evaluate two individuals. In addition, a crowding measure hybridized with interval normalization is further used to reflect the distribution of optimal solutions. Finally, the experimental results demonstrate the effectiveness of the proposed algorithm.