Many different algorithms can be used to optimize the design of spatial measurement networks. For the spatial interpolation of environmental variables in routine and emergency situations, computation time and interpolation accuracy are important criteria to evaluate and compare algorithms. In many practical situations networks are not designed from scratch but instead the objective is to modify an existing network. The goal then is to add new measuring stations optimally or to withdraw existing stations with as little damage done as possible. The objective of this work is to compare the performance of different optimization algorithms for both computation time and accuracy criteria. We describe four algorithms and apply these to three datasets. In all scenarios the mean universal kriging variance (MUKV) is taken as the interpolation accuracy measure. Results show that greedy algorithms that minimize the information entropy perform best, both in computing time and optimality criterion.