Distributed Job Shop Scheduling Problem (DJSSP) is a generalization of the classic job shop scheduling problem (JSSP), characterized by simultaneously assigning jobs to different factories/workshops and determining their processing sequence. Due to energy efficient and productivity significantly affect the profits of enterprises, two criteria (i.e., makespan and total energy consumption) are used to evaluate the DJSSP. In this paper, a mixed integer programming model is developed, and a multi-objective artificial bee colony algorithm (MOABC) is proposed to evaluate the trade-off between the two objectives. In the MOABC, a solution encoding is represented by three parts: a factory assignment vector, a job permutation vector and a machine speed selection matrix. Meanwhile, a solution decoding is designed to present the scheduling scheme. A numerical experiment is designed to verify the effectiveness of the proposed MOABC. The experimental result demonstrates that the proposed MOABC can generate better Pareto solutions than NSGA-II and SPEA2 for the DJSSP.