Grid scheduling which aims at improving resource utilization and grid application performance is a key concern in grid. Currently, much research can be found about grid scheduling and some algorithms on it were proposed. However, since grid resources are autonomic, distributed and their status change over time, those scheduling algorithms did not fit for the cases well. In this paper, a cache based feedback grid scheduling (CBFS) approach is presented to capture the dynamics and impact of simultaneously co-allocated tasks in a grid. In this approach, grid scheduler utilizes recent resource performance data, such as recent task submitting time and execution time of task which are kept in cache and a feedback approach to engineer load balancing across multiple grid resources. After comparing this dynamic grid scheduling approach with previous research, it is found that CBFS is more generous than other scheduling approaches. Experimental results demonstrate that this approach diminishes latency and contributes to the overall grid load balancing, which significantly improves resource utilization and response time of tasks.