Accurate intraoperative imaging is essential for successful endovascular aneurysm repair (EVAR), enabling navigation of complex vascular anatomies and precise device placement. Surgeons often acquire multiple angiographic views, but manual viewpoint selection can lead to repeated C-arm repositioning, increased radiation exposure, and prolonged procedures. While recent methods automate view planning using vascular geometry and pose estimation, they often assume unrestricted C-arm mobility and overlook device-specific spatial constraints. In this work, we propose a novel constraint-aware, automated multi-view planning framework that leverages preoperative CTA data to generate optimized X-ray views tailored to procedural and equipment limitations. Our method starts with vessel segmentation, centerline extraction, and vessel graph construction. A planning route is defined along the target centerline, from which discrete points are sampled as local region centers. For each center, we define a region of interest and solve a constrained optimization problem to determine the optimal viewing orientation. The objective function combines two criteria: vessel spread area, computed via the convex hull area of the projected centerline, and inter-region projection separation, which promotes spatially clear views by minimizing overlap. We validated our framework on an in-house preoperative CTA dataset from 27 patients. Both qualitative and quantitative results demonstrate improved region visibility, spatial separation, and continuity of optimal viewing poses along the vascular path.