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Data grid supports a variety of scientific applications that require access to large amount of data with various qualities of service requirements. Ensuring efficient access to such large and widely distributed datasets is hindered by high latencies. Effective scheduling in grid can reduce the amount of data transferred among nodes by dispatching a job to node where most of the required data is available. In addition to efficient scheduling, dynamic replication strategies are regarded as one of the major optimization technique for reducing access latency. In this paper, we propose a two-phase dynamic replication strategy coupled with two-stage job scheduling to provide an integrated environment for efficient access to data and job scheduling. Simulation results demonstrate that JS2DR2 improves the data access time in data grids and the gain increases with the increase in work load.