The Segmentation Parameter Tuner (SPT) is a tool designed for automatic tuning of segmentation parameters. In SPT, the goodness of a set of parameter values is given by the level of agreement between the segmentation result and a given reference (representing the desired outcome) quantified by a metric selected by the user (empirical discrepancy methods). This metric is used as the fitness function of an optimization algorithm that searches the parameter space for the minimum value, which is expected to correspond to the segmentation outcome most similar to the reference. SPT 3.1 offers many interesting features such as: five segmentation algorithms (for Optical, Hyperspectral and SAR images), four optimization algorithms (stochastic and direct search optimization methods) and seven discrepancy metrics (pixel and object-based). This paper describes the optimization procedure underlying SPT 3.1, the features added to this version as well as an experiment that illustrates the operation of the tool.