Meta-heuristics typically takes long time to search optimality from huge amounts of data samples for applications like communication, medicine, and civil engineering. Therefore, parallelizing meta-heuristics to massively reduce runtime is one hot topic in related research. In this paper, we propose a MapReduce modified cuckoo search (MRMCS), an efficient modified cuckoo search (MCS) implementation on a MapReduce architecture-Hadoop. MapReduce particle swarm optimization (MRPSO) from a previous work is also implemented for comparison. Four evaluation functions and two engineering design problems are used to conduct experiments. As a result, MRMCS shows better convergence in obtaining optimality than MRPSO with two to four times speed-up. Meta-heuristics typically takes long time to search optimality from huge amounts of data samples for applications like communication, medicine, and civil engineering. Therefore, parallelizing meta-heuristics to massively reduce runtime is one hot topic in related research. In this paper, we propose a MapReduce modified cuckoo search (MRMCS), an efficient modified cuckoo search (MCS) implementation on MapReduce architecture-Hadoop. MapReduce particle swarm optimization (MRPSO) from a previous work is also implemented for comparison. problems are used to conduct experiments. As a result, MRMCS shows better convergence in accessibility optimal than MRPSO with two to four times speed-up.