Abstract: We study the problem of generating coherent and correct intermediate solution steps for math word problems (MWPs). Solutions to MWPs with step-by-step explanations are valuable, especially in education, to help students better comprehend problem-solving strategies. Most existing approaches narrowly focus on obtaining the final correct answer. A few recent approaches leverage intermediate solution steps to improve final answer correctness but often cannot generate coherent steps with a clear solution strategy. Contrary to existing work, we focus on improving the correctness and coherence of the intermediate solutions steps. We propose a step-by-step planning method for intermediate solution generation, which strategically plans the generation of the next solution step based on the MWP and the previous solution steps. Our approach first plans the next step by predicting the necessary math operation needed to proceed given history steps, then generates the next step, token-by-token, by prompting a language model with the predicted math operation. Experiments on the GSM8K dataset demonstrate that our method improves the accuracy and interpretability of the solution by both automatic metrics and human evaluation.