In cartographic generalization, the selection is an often-used method to adjust information density in a map. This paper deals with methods for selecting point features for a specific scale with numerical attributes, such as population, elevation, or visitors. With the Label Grid approach and the method of Functional Importance, two existing approaches are described, which have not been published in the scientific literature so far. They are explained and illustrated in the method chapter for better understanding. Furthermore, a new approach based on the Discrete Isolation measure is introduced. It combines the spatial position and the attribute's value and is defined as the minimum distance to the nearest point with a higher value. All described selection methods are implemented and made available as Plugins named “Point selection algorithms” for QGIS. Based on this implementation, the three methods are compared regarding runtime, parameterization, legibility, and generalization degree. Finally, recommendations are given on which data and use cases the approaches are suitable. We see digital maps with multiple scales as the main application of those methods. The possibilities of labeling the selected points are not considered within the scope of this work.