Small hydropower plants (SHPs), especially run-of-river types, offer an environmentally friendly and cost-efficient solution for rural electrification. However, the conceptual stage—particularly site selection and layout configuration—involves complex trade-offs among cost, energy output, and technical constraints. Traditional design methods often rely on heuristics and experience, resulting in suboptimal outcomes. Evolutionary algorithms (EAs), including genetic algorithms (GAs) and multi-objective variants such as NSGA-II, provide a powerful alternative by efficiently exploring large, nonlinear solution spaces. This paper presents how EAs can be applied to optimize SHP layout decisions at the pre-design stage. Key components of the optimization model are described, including objective functions (e.g., cost minimization, energy maximization), constraints (e.g., topography, flow limits), and encoding strategies. A numerical example is provided to illustrate cost-energy trade-offs under different design scenarios. The effectiveness of Gas is demonstrated using simplified calculations, revealing potential cost reductions of up to 70% compared to conventional layouts. Moreover, multi-objectives EAs generate Pareto-optimal sets of design alternatives, allowing decision-makers to balance investments versus performance. The paper concludes that integrating EAs into SHP planning enhances the exploration of viable layouts, enables transparent evaluation of trade-offs, and supports the development of more economically and technically robust hydropower projects.