A huge amount of remotely sensed data is acquired daily and many algorithms for processing the data are complex. Grid computing, which is different from conventional distributed computing, can harness the power of many computers in a network to solve problems. Grid computing has become an exciting way of solving the large-scale or complex problems. To apply the grid computing to remote sensing information, a remote sensing information grid analysis and service node (RSIGN) has been established in Telegeoprocessing Group, Institute of Remote Sensing Applications, Chinese Academy of Sciences. The management and distribution of the tasks are key problems in RSIGN node. How to distribute and manage the tasks has significant influence on the efficiency of the whole grid system. In this paper, we will discuss two main strategies for RSIGN node: geometric parallel and algorithm parallel. Different problems in the grid node need different strategies. On one hand, the data to be processed and analyzed can be divided into sub-datasets and processed on different computers. On the other hand, some algorithms can be rewritten to fit the parallel rules. In the latter case, different steps of processing the whole dataset can be executed on different computers. Through the experience of applied the both strategies on RSIGN node, we compared and evaluated the two strategies, and figured out the merits and disadvantages of two different strategies when applied on remotely sensed information analysis. At the end of the paper, we discuss how to choose the best task managing strategy for different problems in remote sensing information processing and analysis.