A multi-objective optimization of a solar air heater with obstacles on an absorber plate is performed for maximum heat transfer and minimum pressure loss. In this work, shape optimization is carried out in conjunction with three-dimensional Reynolds-averaged Navier-Stokes analysis and two basic surrogate models: the response surface approximation and the Kriging models. Three geometric variables (the ratio of the obstacle height to the height of the duct; the ratio of the transverse pitch to the base length of the obstacle; and the angle of attack) were used as design variables for the optimization. The average Nusselt number and friction factor were used to define the two objective functions. The Latin hypercube sampling method was used to select the design points in the design space. A hybrid multi-objective genetic algorithm coupled with the surrogate model was used to find the Pareto-optimal solutions. The representative Pareto-optimal solutions were selected to study the trade-off between the two objectives. The response surface approximation model leads to a better set of non-dominated solutions over a wide range of functional space than the Kriging model. The optimization results show that the objective functions are significantly affected by the design variables, and the constructed surrogate models show good prediction accuracies for the objective functions. A performance factor was used to study the thermal hydraulic performance of the Pareto-optimal solutions. (C) 2015 Elsevier Ltd. All rights reserved.