The Industrial Internet of things (IIoT), which consists of massive IoT devices and wireless access points in industrial infrastructures to acquire intelligent services, has been considered as a vital physical information platform for implementing Industry 4.0. Further, mobile edge computing (MEC) has brought an opportunity to accelerate the development of IIoT. However, the existing MEC methods may not be directly used for IIoT scenarios due to the large size of IIoT devices and the characteristics of the applications, as well as the limited and heterogeneous resources of edge servers. In view of this, the computation offloading and resource allocation are formulated as a multi-objective optimization problem, and an end-edge-cloud collaborative intelligent optimization method is devised in this paper. Comprehensive experiments and evaluations are carried out to prove the effectiveness and efficiency of our proposed method with regard to the energy consumption and time consumption of IIoT devices, as well as resource utilization and load balancing of edge servers.