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To find a parking space, valet parking drivers have to travel a lot, which leads to carbon dioxide (CO2) emissions. In order to reduce these emissions, it is essential to understand a user’s needs and criteria when searching for a parking space. Several selection criteria are considered when allocating a parking space. Recent research on parking space management mentions several parameters that have an impact on the choice of a parking space: namely, the traffic situation, the availability of each parking lot in question, and the cost of parking, etc. In this article, we discuss a new criterion: the physical condition of the driver in the management of parking spaces; the identification of the driver’s bodily fragility. We also propose MCDM as a parking space allocation model that best meets the cost–benefit convention. This reflection leads us to evaluate MCDM methods in the field of intelligent parking management. Therefore, we conducted a comparison between the most recent multi-criteria decision making methods used by researchers, namely, CODA, EDAS, TOPSIS, and WASPAS. The CRITIC method was used in this paper to objectively determine the weight of each criterion. A new approach is proposed to evaluate and select the best MCDM method. Indeed, we propose a method that computes the “average inter-item correlation SW”, a combination of the “average inter-item correlation” and the SW coefficient. This approach allows us to efficiently compute the correlation between a method and the set of methods while favoring the cells with the best ranking. A case study is presented to illustrate the MCDM approach to parking space allocation and evaluation. The proposed system provides drivers with services such as intelligent parking decisions, taking into account the human aspect while reducing energy consumption, driving time, and traffic congestion caused by searching for available parking spaces.