The objective of this research is to lower the cost of solar-powered drip irrigation systems and thus make them more accessible to smallholder farmers, who operate farms that are less than 2 ha and normally located in rural communities. Current solar-powered drip irrigation systems that are conventionally sized are expensive due to their oversized pumps and many solar panels. These systems are generally not utilized by smallholder farmers because they are cost prohibitive. Although other irrigation technologies can be less expensive to the farmer, drip irrigation has been shown to reduce water waste and increase yields more than other irrigation methods, two benefits that could impact the livelihood of smallholder farmers, who manage 475 million farms worldwide. Previous work has been conducted to lower the cost of these systems including developing low pressure drip irrigation emitters and cost optimizing a model of the system. The work presented combines the use of low pressure emitters with a unique system-level model and optimization to lower the cost even further. The necessary components that make up a solar-powered drip irrigation system were explored and a model was created that predicts life cycle cost and performance of the system. It was found that the components that make up the system could be grouped into modules, and that these modules were highly interdependent. Thus, the modules were detailed extensively so that a holistic and flexible model could be created. The model was then optimized and a sensitivity analysis was conducted to investigate the key parameters that affected the system's cost and design for a baseline case of a 1 ha olive orchard in Morocco. The optimization was built to either minimize the system cost or to maximize the farmer's profit. For the same sample case this optimization was shown to reduce the life cycle cost by 62% compared to a conventionally sized system. The results of the analysis demonstrated that for smallholder farms direct-drive systems, or systems that do not use energy storage options, were cost optimal. Additionally, a reliability metric could be imposed that allowed for a 7-13% reduction in cost for 10% reduction in reliability. This reduction in reliability led to negligible reduction in yield for the water stress resistant crop of olives that was used. The designs that were built to maximize profits for the smallholder farms each had a reliability between 0-10% due to this reduction in cost. Additionally, the robustness of the model was tested by ensuring the repeatability of the convergence and executing the optimization for various weather conditions. The system model was validated through field trials that took place over a year with one solar-powered drip irrigation system set up on an olive orchard in Morocco and another on a citrus orchard in Jordan. For the trials the systems were oversized to ensure that the irrigation demand was met with 100% reliability for unforeseeable weather variations. The measured results of the system's delivered water as well as the operational pressure, flow, and power were similar to those predicted in simulation. The differences between what was measured and what was simulated were mostly due to unaccounted pressure and flow variations in the system as well as a mismatch between the simulated crop water demand and the irrigation delivered, which was calculated by local research staff and then input into the pump controller. Further testing will need to be conducted in order to validate the optimization of the model.