Multitask learning has been successfully used in handling multiple related tasks simultaneously. In reality, there are often many tasks to be solved together, and the relatedness between them is unknown in advance. In this article, we focus on the multitask genetic programming (GP) for the dynamic flexible job shop scheduling (DFJSS) problems, and address two challenges. The first is how to measure the relatedness between tasks accurately. The second is how to select task pairs to transfer knowledge during the multitask learning process. To measure the relatedness between DFJSS tasks, we propose a new relatedness metric based on the behavior distributions of the variable-length GP individuals. In addition, for more effective knowledge transfer, we develop an adaptive strategy to choose the most suitable assisted task for the target task based on the relatedness information between tasks. The findings show that in all of the multitask scenarios studied, the proposed algorithm can substantially increase the effectiveness of the learned scheduling heuristics for all the desired tasks. The effectiveness of the proposed algorithm has also been verified by the analysis of task relatedness and structures of the evolved scheduling heuristics, and the discussions of population diversity and knowledge transfer.