Grid computing is the principle in utilizing and sharing large-scale resources to solve the complex scientific problem. Under this principle, Grid environment has problems in providing flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources. However, the major problem is in optimal job scheduling, which grid nodes need to allocate the ... [Show full abstract] resources for each job. This paper proposes the model for optimization of jobs scheduling in Grid environment. The model presents the strategies of allocating jobs to different nodes. We develop the model based on three classifications (Heavy workload, Medium workload, Light workload) and depends on jobs run time. We have designed the model by using Fuzzy c-mean clustering technique for predicting the characterization of jobs and optimization jobs scheduling. This prediction and optimization engine will provide historical information in put into jobs scheduling. This paper presents the need for such a prediction and optimization engine and discusses such approach. Simulations runs demonstrate that our algorithm leads to the best results than the traditional algorithms for scheduling policies used in Grid environment.