Engineered products can impact the day-to-day life of their users and other stakeholders. These impacts are often referred to as the product's social impacts. Products have been known to impact the people who use them, design them, manufacture them, distribute them, and the communities where they exist. Currently, there are few methods that can help an engineer identify, quantify, predict, or improve a product's social impact. Some companies and organizations have tried to identify their impacts and, for example, set goals for achieving more sustainable business practices. However, engineers, in large part, do not have methods that can help improve the sustainability and social impacts of their products. Without new methods to help engineers make better product decisions, products will continue to have unanticipated negative impacts and will likely not reach their true social impact potential. Engineers working in the field of Engineering for Global Development (EGD) are especially in need of methods that can help improve the social impacts of their products. One of the purposes of creating products in EGD is to help solve problems that lead to improved quality of life for people and communities in developing countries. The research in this dissertation presents new methods developed to help engineers understand, predict, and improve the social impact of their products. Chapter 2 introduces the Product Impact Metric, a simple metric engineers can use to quantify their products impact on improving the quality of life of impoverished individuals in developing countries. Chapter 3 introduces a method that engineers can use to create product-specific social impact metrics and models. These models are used to predict the social impacts of an expanded US-Mexico border wall on immigrants, border patrol officers, and local communities. Chapter 4 shows a method that allows engineers to create social impact models for individuals within a population. Using data available through online databanks and census reports, the author predicts the social impact of a new semi-automated cassava peeler on farmers in the Brazilian Amazon. In Chapter 5, the author presents a method for engineers to optimize a product according to its social impact on multiple stakeholders. Inspired by existing literature on multi-stakeholder decision making, eight different optimization problem formulations are presented and demonstrated in an example with the cassava peeler. Chapter 6 presents the author's experience in co-designing a semi-automated cassava with the Itacoatiara Rural Farming Cooperative. The peeler was designed and built by the author and is used as the example in Chapters 4 and 5. Finally, Chapter 7 shows the conclusions the author has in completing this research. Comments are made as to the difficulties encountered in this research (specifically data quality and validation), and the author makes suggestions of possible future work.