Providing fast, reliable and transparent access to data to all users within a community is one of the most crucial functions of data management in a Grid environment. Replication strategies are regarded as one of the major optimization techniques for reducing access latency, improving data locality, and increasing robustness, scalability, and performance for the data grids. Compared with economy-based strategies implemented in OptorSim, utility-based strategies proposed in this paper, which make "buying" or replacement decision based on the utility model, achieve better performance in job execution cost and storage consumption with the same jobs being finished in three different type data grids. The simulation experiments also show that the larger the test data grid scale is, the more the job execution cost saved, which makes our method have a greater advantage in continuously growing data grids.