This brief proposes a fuzzy gain scheduling strategy with an application on parallel parking car-like robots. First, the fuzzy gain scheduling strategy is introduced as a combination of a local path tracking controller and fuzzy rule based techniques. In light of human driver experience in parallel parking, the control goal is achieved by repeatedly scheduling parameters and tracking local paths. Meanwhile, a time-varying fuzzy sliding mode controller (TFSC) is developed as the local tracking controller to guarantee robust performance and fast tracking response for a segment of preplanned reference path. Different to traditional gain scheduling, the overall controller combining the TFSC and a fuzzy gain scheduler has advantages in regards of 1) a small data base; 2) an enlarged workspace of interest; and 3) allowing zero velocity crossing. Then, the scenario of parallel parking car-like robots is implemented in presence of nonholonomic and input saturation constraints. Finally, numerical simulation and practical experiment are carried out to show the expected performances.