This thesis presents an investigation into the optimization of Graph Neural Network (GNN) architectures using the Graph Neural Architecture Search (GraphNAS) framework, an automated system that employs reinforcement learning techniques. The primary goal of this research is to enhance the performance of GNNs by systematically discovering and evaluating potential architectures tailored for specific types of graph data, such as those found in social networks, communication systems, and chemical structures. We initiated our study by evaluating various architecture search libraries and selected GraphNAS for its promising initial results and compatibility with our project objectives. Our methodology involved configuring GraphNAS to generate potential GNN architectures, beginning with basic Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT), and later expanding the search space to include diverse layer types and activation functions. We introduced a ”None” layer to investigate the potential for more sparse and efficient network structures and adjusted our strategy based on the initial performance insights, focusing on optimizing smaller, more efficient networks. Our results demonstrate that GraphNAS can effectively identify high-performing GNN architectures, with most configurations achieving remarkable accuracy levels above 0.9. We also explored the challenges of building a regressor to predict the performance of these architectures using a synthetically generated dataset, addressing issues related to data imbalance and model underfitting. This work contributes to the field of machine learning by demonstrating the efficacy of automated architecture search in optimizing GNNs, offering insights into the complexities of designing neural networks for graph-based data, and suggesting directions for future research to further improve the automation and efficiency of GNN design.