Despite advancements in Automatic Machine Learning (AutoML), industries encounter challenges in implementation due to data privacy concerns and the costs of centralized data storage. Federated Learning (FL) provides a decentralized approach, allowing multiple clients to collaboratively train models without sharing their datasets. However, many existing FL techniques utilize pre-defined model architectures from centralized environments, which may not be optimal for the non-iid data distributions commonly found among FL clients. This paper introduces AgingFedNAS, a framework designed to automate model design in FL by employing an evolutionary approach to jointly optimize neural architectures and hyperparameters. Comprehensive experiments conducted on heterogeneous data splits from CIFAR-10, Shakespeare, FEMNIST, Tiny-ImageNet, and a medical breast density classification dataset demonstrate that AgingFedNAS outperforms state-of-the-art FL frameworks, including FedAvg, FEATHERS, FedEx, and FedNAS, particularly in non-iid conditions. Notably, AgingFedNAS achieves an accuracy of 89.8% on CIFAR-10, exceeding the best baseline, FEATHERS, by 3.78%. In the breast density classification task, it surpasses FedNAS by 1.3%, achieving up to 3.2% higher accuracy for specific clients under non-iid scenarios. Additionally, in highly heterogeneous data environments, AgingFedNAS shows a 2.3% accuracy improvement on CIFAR-10 compared to the top-performing baseline.