Measurement system configuration is an important task in structural health monitoring in that decisions influence the performance of monitoring systems. This task is generally performed using only engineering judgment and experience. Such approach may result in either a large amount of redundant data and high data‐interpretation costs, or insufficient data leading to ambiguous interpretations. This paper presents a systematic approach to configure measurement systems where static measurement data are interpreted for damage detection using model‐free (non‐physics‐based) methods. The proposed approach provides decision support for two tasks: (1) determining the appropriate number of sensors to be employed and (2) placing the sensors at the most informative locations. The first task involves evaluating the performance of measurement systems in terms of the number of sensors. Using a given number of sensors, the second task involves configuring a measurement system by identifying the most informative sensor locations. The locations are identified based on three criteria: the number of non‐detectable damage scenarios, the average time to detection and the damage detectability. A multi‐objective optimization is thus carried out leading to a set of non‐dominated solutions. To select the best compromise solution in this set, two multi criteria decision making methods, Pareto‐Edgeworth‐Grierson multi‐criteria decision making (PEG‐MCDM) and Preference Ranking Organization METhod for Enrichment Evaluation (PROMETHEE), are employed. A railway truss bridge in Zangenberg (Germany) is used as a case study to illustrate the applicability of the proposed approach. Measurement systems are configured for situations where measurement data are interpreted using two model‐free methods: Moving Principal Component Analysis (MPCA) and Robust Regression Analysis (RRA). Results demonstrate that the proposed approach is able to provide engineers with decision support for configuring measurement systems based on the data‐interpretation methods used for damage detection. The approach is also able to accommodate the simultaneous use of several model‐free data‐interpretation methods. It is also concluded that the number of non‐detectable scenarios, the average time to detection and the damage detectability are useful metrics for evaluating the performance of measurement systems when data are interpreted using model‐free methods.