This paper explores an indirect approach to the Three-dimensional Multi-pipe Routing problem. Variable length pipelines are built by letting a virtual robot called a turtle navigate through space, leaving pipe segments along its route. The turtle senses its environment and acts in accordance with commands received from heuristics currently under evaluation. The heuristics are evolved by a Gene Expression Programming based Learning Classifier System. The suggested approach is compared to earlier studies using a direct encoding, where command lines were evolved directly by genetic algorithms. Heuristics generating higher quality pipelines are evolved by fewer generations compared to the direct approach, however the evaluation time is longer and the search space is more complex. The best evolved heuristic is short and simple, builds modular solutions, exhibits some degree of generalization and demonstrates good scalability on test cases similar to the training case.