The task of AMR-to-text generation is to gen- erate grammatical text that sustains the seman- tic meaning for a given AMR graph. We at- tack the task by first partitioning the AMR graph into smaller fragments, and then gener- ating the translation for each fragment, before finally deciding the order by solving an asym- metric generalized traveling salesman prob- lem (AGTSP). A Maximum Entropy classifier is trained to estimate the traveling costs, and a TSP solver is used to find the optimized solu- tion. The final model reports a BLEU score of 22.44 on the SemEval-2016 Task8 dataset.