Dynamic flexible job shop scheduling is an important combinatorial optimisation problem that covers valuable practical applications such as order picking in warehouses and service allocation in cloud computing. Machine assignment and operation sequencing are two key decisions to be considered simultaneously in dynamic flexible job shop scheduling. Genetic programming has been successfully and widely used to learn scheduling heuristics, including a routing rule for machine assignment and a sequencing rule for operation sequencing simultaneously. There are mainly two types of learning strategies to evolve scheduling heuristics, i.e., learning one rule by fixing the other rule, and learning the routing rule and the sequencing rule simultaneously. However, there is no guidance on which learning strategy to use in specific cases. To fill this gap, this paper provides a comprehensive study of learning strategies on scheduling heuristics of genetic programming in dynamic flexible job shop scheduling by comparing five learning strategies, including two strategies that are extended from the existing studies. The results show that learning two rules simultaneously, either using cooperative coevolution or multi-tree representation, is more effective than only learning one type of rule. Cooperative coevolution is recommended if an algorithm aims to handle a problem by dividing it into small sub-problems, and focuses on the characteristics of routing rule and sequencing rule. Genetic programming with multi-tree representation that treats the routing rule and the sequencing rule as an individual, is preferred to reduce the complexities of algorithms.