How do we evaluate the performance of an indoor positioning system? In addition, in which way can the system be optimally tuned for a certain environment? These are the questions addressed in this study. We propose a practical, cost efficient methodology for evaluating and tuning indoor positioning systems. The methodology has two main phases. In the first online recording phase, the ground truth information is gathered, and raw signals are recorded. In the second phase, offline positioning algorithms utilise the recorded information to infer position estimations which can then be precisely evaluated. An automatic parameter optimization methodology, which recommends optimal tunings for the positioning algorithm, is presented as a key utility of this work. An overall advantage of the proposed method is the fact that the recorded data guarantee the repeatability of tests, and allow consistent comparisons among different algorithms, creating the perspective of a testbed based on real data. The implementation of the methodology is exemplified with the presentation of the GpmLab Android application and the GpmStudio desktop platform, tools which assist our main positioning framework, the Global Positioning Module (GPM).