The deployment of Large Language Models (LLMs) faces significant challenges due to high computational costs, driving the demand for effective pruning techniques. Existing structured pruning methods employ uniform compression rates across network layers, neglecting the varying importance of different network depths. To address this limitation, we propose a novel optimization framework that directly minimizes global capability loss through layer-adaptive pruning rates. The framework formulates the pruning task as a combinatorial optimization problem constrained by a total parameter budget, and an efficient dynamic programming solution is derived to determine optimal layer-wise compression rates. Experiments demonstrate that, when tuning is not included, our approach achieves comparable performance with state-of-the-art methods at high pruning rates (37 - 50% reduction), and shows significant advantages at low pruning rates (25% reduction). When tuning is included, our method achieves the best performance among the compared methods.