This paper presents a meta-heuristic algorithm for solving the Flexible Job Shop Scheduling Problem (FJSSP). This strategy, known as Iterative Flattening Search (IFS), iteratively applies a relaxation-step, in which a subset of scheduling decisions are randomly retracted from the current solution; and a solving-step, in which a new solution is incrementally recomputed from this partial schedule. This work contributes two separate results: (1) it proposes a constraint-based procedure extending an existing approach previously used for classical Job Shop Scheduling Problem; (2) it proposes an original relaxation strategy on feasible FJSSP solutions based on the idea of randomly breaking the execution orders of the activities on the machines and opening the resource options for some activities selected at random. The efficacy of the overall heuristic optimization algorithm is demonstrated on a set of well-known benchmarks.