Design of Experiments (DOE) is a powerful and systematic approach used in various fields to efficiently plan, execute and analyze experiments. This review provides a comprehensive overview of the application of DOE in the context of screening and optimization of experiments. Screening experiments are employed to identify significant factors or variables that influence a response, while optimization experiments aim to fine-tune these factors to achieve optimal outcomes. The review introduces the fundamental concepts of DOE, including factors, levels and response variables. It explores the various types of designs commonly used in screening, such as full factorial, Taguchi and Plackett-Burman designs, highlighting their advantages and limitations. The article also delves into the practical aspects of designing and conducting experiments with DOE, emphasizing the importance of proper planning and statistical analysis. Key topics covered include the selection of appropriate designs, sample size determination and data analysis techniques. Furthermore, the review touches upon the integration of computer-aided tools and software for DOE, making the process more efficient and accessible. The review also discusses the impact of DOE on resource and time savings, as well as its potential for enhancing product quality and process efficiency. This review underlines the significance of Design of Experiments in screening and optimization, offering insights into its versatility, practical implementation and the substantial benefits it can bring to research and industry. Researchers, practitioners and decision-makers in various domains will find this review valuable in harnessing the full potential of DOE for improving experimentation and decision-making processes.