Protein folding problem is a classical non-deterministic polynomial(NP) hard problem in bioinformatics. The energy landscape paving (ELP) method is a class of heuristic global optimization algorithm. This paper applies the ELP method to simulate protein folding conformations for the hydrophobic-polar (HP) model on the face-centered-cube (FCC) lattice. By putting forward a new update mechanism of the histogram function in ELP and incorporating the generation of initial conformation based on the greedy strategy and the neighborhood search strategy based on pull-moves into ELP, an improved energy landscape paving (ELP+) method is put forward for the protein folding problem on the FCC lattice model. We test the method on nine benchmark sequences. The lowest energies by ELP are as good as or better than those of other methods in the literature for all instances. Computational results show that ELP is an effective method for protein folding problem on FCC lattice model.