This paper proposes a method for extracting the intrinsic parameters of a photovoltaic (PV) generator by using shuffled complex evolution (SCE) technique for a double-diode PV model. The characteristic equation of a double-diode PV presents a nonlinear behavior and the determination of the intrinsic parameters from a $I \times V$ experimental curve requires the use of nonlinear optimization methods. To evaluate the accuracy of the SCE technique for extracting the intrinsic PV parameters, a comparison with other well-known methods is presented; in particular, analytic method, Levenberg–Marquardt, genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO) are considered. This comparison is performed by using statistical analysis and by estimating the relative error of parameter values; it has been applied to an unknown PV module and to a known PV cell. The obtained results showed that, compared with other evolutionary methods (GA, DE and PSO), the SCE presents the lowest computational time and requires less iterations/generations to converge. All the results prove that the proposed method is feasible, faster, and presents better results than the conventional ones.