The invention discloses a MapReduce optimizing method suitable for iterative computations. The MapReduce optimizing method is applied to a Hadoop trunking system. The trunking system comprises a major node and a plurality of secondary nodes. The MapReduce optimizing method comprises the following steps that a plurality of Hadoop jobs submitted by a user are received by the major node; the jobs are placed in a job queue by a job service process of the major node and wait for being scheduled by a job scheduler of the major node; the major node waits for a task request transmitted from the secondary nodes; after the major node receives the task request, localized tasks are scheduled preferentially by the job scheduler of the major node; and if the secondary nodes which transmit the task request do not have localized tasks, prediction scheduling is performed according to task types of the Hadoop jobs. The MapReduce optimizing method can support the traditional data-intensive application, and can also support iterative computations transparently and efficiently; dynamic data and static data can be respectively researched; and data transmission quantity can be reduced.