Acquiring information on the Earth's electric and magnetic properties is a critical task in many geophysical applications. Since electromagnetic (EM) geophysical methods are based on nonlinear relationships between observed data and subsurface parameters, designing experiments that provide the maximum information content within a given budget can be quite difficult. Using examples from direct-current electrical and frequency-domain EM applications, we review four approaches to quantitative experimental design. Repeated forward modelling is effective in feasibility studies, but may be cumbersome and time-consuming for studying complete data and model spaces. Examining Fréchet derivatives provides more insights into sensitivity to perturbations of model parameters, but only in the linear space around the trial model and without easily accounting for combinations of model parameters. A related sensitivity measure, the data importance function, expresses the influence each data point has on determining the final inversion model. It considers simultaneously all model parameters, but provides no information on the relative position of the individual points in the data space. Furthermore, it tends to be biased towards well resolved parts of the model space. Some of the restrictions of these three methods are overcome by the fourth approach, statistical experimental design. This robust survey planning method, which is based on global optimization algorithms, can be customized for individual needs. It can be used to optimize the survey layout for a particular subsurface structure and is an appropriate procedure for nonlinear experimental design in which ranges of subsurface models are considered simultaneously.