Population-based sampling methods, such as evolutionary algorithms, are generally applied to solve large optimization problems with rugged energy landscapes. By monitoring the population and enabling communication, these techniques allow one to control and guide the sampling process. Due to these features, population-based sampling methods can be applied to a wide spectrum of problems. With a huge search space and a funnel-like energy landscape composed of multiple local attraction basins, protein structure prediction falls in the range of problems on which population-based sampling methods typically perform well. Here, we present the main variants of evolutionary algorithms, and show through the example of EdaRose how population-based sampling methods can be applied to protein structure prediction to effectively search the conformational space while maintaining a balance between exploration and exploitation.