In this paper, the placement problem on FPGAs is faced using Thermodynamic Combinatorial Optimization (TCO). TCO is a new combinatorial optimization method based on both Thermodynamics and Information Theory. In TCO two kinds of processes are considered: microstate and macrostate transformations. Applying the Shannon's definition of Entropy to microstate reversible transformations, a probability of acceptance based on Fermi-Dirac statistics is derived. On the other hand, applying thermodynamic laws to reversible macrostate transformations, an efficient anneling schedule is provided. TCO has been compared with Simulated Annealing (SA) on a set of benchmark circuits for the FPGA placement problem. TCO has achieved large time reductions with respect to SA, while providing interesting adaptive properties.