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Evolutionary algorithms (EAs) are widely used nature-inspired optimization methods capable of solving complex and high-dimensional problems across science and engineering. Foundational paradigms such as genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming have expanded into multi-objective, surrogate-assisted, hybrid, and large-scale variants, broadening their applicability to dynamic and datadriven environments. This survey provides a structured review of EAs from a domain-centric perspective, focusing on how different techniques are designed for engineering problems. Applications are examined across renewable energy, civil and structural engineering, electronics, industrial optimization, healthcare, robotics, and smart cities. We present an updated taxonomy of classical and emerging algorithms, consolidate recent application studies, and review benchmarking and reproducibility practices essential for fair evaluation. Key challenges including scalability, constraint handling, and exploration–exploitation balance are discussed alongside future directions such as EA–deep learning integration, federated optimization, and interpretable evolution. This survey offers an updated view of EAs and their engineering relevance.