The widespread adoption of Location-Based Services (LBSs) has come with controversy about privacy. While leverag-ing location information leads to improving services through geo-contextualization, it rises privacy concerns as new knowledge can be inferred from location records, such as home/work places, habits or religious beliefs. To overcome this problem, several Location Privacy Protection Mechanisms (LPPMs) have been proposed in the literature these last years. However , every mechanism comes with its own configuration parameters that directly impact the privacy guarantees and the resulting utility of protected data. In this context, it can be difficult for a non-expert system designer to choose appropriate configuration parameters to use according to the expected privacy and utility. In this paper, we present a framework enabling the easy configuration of LPPMs. To achieve that, our framework performs an offline, in-depth automated analysis of LPPMs to provide the formal relationship between their configuration parameters and both privacy and the utility metrics. This framework is modular: by using different metrics, a system designer is able to fine-tune her LPPM according to her expected privacy and utility guarantees (i.e., the guarantee itself and the level of this guarantee). To illustrate the capability of our framework, we analyse Geo-Indistinguishability (a well known differentially private LPPM) and we provide the formal relationship between its configuration parameter and two privacy and utility metrics.