Detecting anomalies in graph data is a crucial research area with extensive applications across various domains, including but not limited to fraud detection, cybersecurity, health monitoring, and system failure prediction. Graph neural networks (GNNs) have recently gained popularity for anomaly detection tasks due to their powerful capability to leverage complex relationships and inherent structural patterns within graph-based datasets. However, manual design and fine-tuning of GNN architectures are typically time-consuming processes that require considerable expertise. Furthermore, many existing GNN methods predominantly emphasize spatial graph information while neglecting important spectral features, thereby limiting their effectiveness in clearly distinguishing anomalies during information aggregation. To address these issues, we introduce PAGNAS, a Performance-Aware Graph Neural Architecture Search framework specifically tailored for anomaly detection. PAGNAS integrates spectral-based GNN ranking, improving its ability to identify anomalies within graph structures accurately. Our framework leverages a neural performance ranking predictor that automates the selection and optimization of GNN architectures, significantly reducing manual design efforts. By focusing on the discovery of optimal architectures rather than strictly predicting their exact performance, PAGNAS efficiently navigates the architectural search space. Empirical evaluations across four benchmark datasets confirm the effectiveness of PAGNAS. It consistently achieves competitive or superior AUC scores compared to state-of-the-art baselines. These results highlight the robustness of PAGNAS in synthesizing high-performing GNN architectures for anomaly detection in attributed graphs.