Instruction tuning plays a crucial role in enhancing large language models (LLMs) to better understand complex user instructions. While various data selection and revision methods have been explored to optimize instruction tuning datasets, they face two main challenges: unreasonable pruning of potentially valuable low-quality data and the persistence of noise or semantic drift during revision. To address these issues, we propose a novel automated iterative framework for instruction data optimization. Our framework introduces Instruction Quality Differentiation to identify valuable high-quality and low-quality data across multiple dimensions. For low-quality data, we propose a Feedback-driven Iterative Refinement mechanism with an "evaluate-refine-review" process and design an Output Alignment module to improve data quality. Experiments on seven public benchmark datasets show that our framework outperforms state-of-the-art methods, achieving 2.09% and 2.60% improvements on the Alpaca and Dolly datasets, respectively, with high data efficiency. Our code and data are available at the anonymous link https://github.com/surihuhang/From-Selection-to-Refinement–Iterative-Optimization-for-Instruction-Data.