Abstract: As an important part of the job shop manufacturing system, the job shop scheduling problem (JSSP) affects the agility and intelligence of the whole enterprise. In the real scenarios, the resource restriction coexists with the process restriction, which makes JSSP become NP-hard. Therefore, there is not yet an applicable method for solving the JSSP. In this paper, a job shop scheduling model combining MAS (multi-agent system) with multi-intelligence algorithms is presented. The proposed model is based on the generalized partial global planning (GPGP) mechanism and utilizes the advantages of static intelligence algorithms with dynamic MAS. A scheduling process from “initialized macro-scheduling” to “repeated micro-scheduling” is designed for large-scale complex problems to enable to implement an effective and widely applicable prototype system for JSSP. Under this scheme, a set of theoretic strategies in the GPGP are summarized in detail. A two-stage multi-objective optimization scheduling is performed and the GPGP-cooperation-mechanism is simulated by using simulation software DECAF for the JSSP. Meanwhile, those simulation results are compared with CNP-cooperation-mechanism and NONE mechanism. The results show that the proposed model based on the GPGP-cooperation-mechanism not only improves the effectiveness, but also reduces the resource cost.