Multi-hop question answering requires retrieving and integrating multiple pieces of evidence distributed across documents. Recent graph-based methods improve multi-hop reasoning through graph traversal over relational evidence structures. However, existing methods rely on independent path ranking during evidence organization, which often produces redundant and reasoning-incomplete evidence sets due to substantial structural overlap among selected graph paths. In this work, we argue that graph-based evidence organization may, to some extent, be formulated as a constrained evidence subgraph optimization problem. Based on this perspective, we propose CROG, a coverage-aware reasoning optimization framework that performs marginal-gain-based path selection to favor previously uncovered reasoning steps while suppressing redundant evidence, with node-budget and connectivity constraints to encourage compact and coherent evidence subgraphs. Experiments on multiple benchmarks demonstrate that CROG achieves superior reasoning performance while using fewer evidence units on average, and analysis based on exhaustive optimal search further validates the effectiveness of the greedy optimization strategy.