Scheduling is an important decision-making process for manufacturing companies. Shop scheduling is a subsection of scheduling related to manufacturing shops. The job shop scheduling problem (JSP) aims to minimize the makespan of a product value of the production process. This issue is related to optimizing the sequence of the jobs on appropriate machines. With globalization, manufacturing shops are scattered around the world. The distributed job shop scheduling problem (DJSP) tries to solve the optimizing sequence on these scattered facilities. DJSP is more complex than JSP. DJSP is solved by exact and heuristic solvers. Exact solvers more time-consuming processes for huge problems. In this work, a discrete version of the spotted hyena optimizer (DSHO) is proposed for solving DJSP. A workload-based facility order mechanism and a greedy heuristic approach are combined with the DSHO algorithm. 80 distributed job shop scheduling problems (DJSP) are solved by DSHO. For evaluating the performance of the DSHO, numerical results of the 480 (2 facilities to 7 facilities) large instances that are derived from well-known JSP benchmarks are compared with four different discrete meta-heuristic algorithms. The experimental results are shown that DSHO is a pioneer solver for DJSP.