The Particle Swarm Optimization (PSO) method and the Genetic Algorithm (GA) were used to derive formulas for determining the velocity and concentration profiles in sheet flows. Specifically, these evolutionary optimization algorithms were used in conjunction with experimental data to determine coefficients and identify parameters for pre-selected formulas. The objective function, defined as the sum-of-squared errors between observed and predicted values of sediment velocity and concentration, was minimized by adjusting the parameter values in the formulas. Two well-known empirical formulas were also applied to the same data. The bias, root mean square error and scatter index were used to evaluate the comparison between predictions and measurements. The results indicated that the errors based on the PSO and GA approaches to predicting sediment parameters were less than those of the existing empirical formulas. Overall, both evolutionary approaches provided formulas that were in good agreement with the experimental data, giving improved descriptions of the vertical distribution of velocity and sediment concentration in the sheet flow for practical purposes. These models also described well the behavior of the velocity and sediment concentration above the sheet flow layer, in contrast with most existing formulas that are applicable only to the sheet flow layer.