The exploitation of frequent item sets has been restricted by the the large number of generated frequent item sets and the high computational cost in real world applications. Meanwhile, maximum length frequent item sets can be efficiently discovered on very large datasets and are useful in many application domains. At present, LFIMiner_ALL is the fastest algorithm for mining maximum length frequent item sets. Exploiting the optimization techniques in LFIMiner_ALL algorithm, we develop the MaxLFI algorithm to discover maximum length frequent item sets by adding our conditional pattern base pre-pruning strategy and evaluating initial length of maximum length frequent item sets to prune the search space. Experimental results on real-world datasets show that our proposed algorithm is faster than LFIMiner_ALL algorithm for mining maximum length frequent item sets.