A wide number of algorithms for surface segmentation in range images have been recently proposed characterized by different approaches (edge filling, region growing...), different surface types (either for planar or curved surfaces) and different parameters involved. Optimization of the parameter set is a particularly critical task since the range of parameter variability is often quite large: parameter selection depends on surface type, sensors and the required speed, which strongly affect performance. A framework for parameters optimization is proposed based on genetic algorithms. Such algorithms allow a general approach that has been successfully applied on different state-of-the-art segmenters and different range image databases.