Abstarct: Harmony Search (HS) algorithm is a new mexta-heuristic method that inspired from musical process. This algorithm proposed as a mextaheuristic method for solving single-objective problem. It became clear that can be used successfully for scientific issues. In this thesis, at first ‎two ‎Harmony ‎Search ‎proposals (MOHS1, MOHS2) ‎are ‎suggesed ‎for ‎solving‎ multiobjective optimization problems. Subsequently, to prove the effectiveness of the proposed scheme, ZDT functions are used as test functions and the algorithm results are compared with results obtained with Nondominated Sorting Genetic Algorithm II (NSGA-II). Although harmony search algorithm for solving optimization problems shows many advantages, its parameters must be defined by users baxsed on the experience and characteristics of the problem.This causes great difficulties for novice users. Subsequently, In order to overcome this difficulty, a Self-Adaptive MultiObjective Harmony Search (SAMOHS) algorithm baxsed on harmony memory variance is proposed in this thesis. For solving multiobjective optimization problems. the proposed algorithm a nondominated sorting and truncating procedure‎ are utilized to update the Harmony Memory.‎‎ Subsequently, results the proposed SAMOHS algorithm are compared with other multi-objective evolutionary algorithms (SPEA2, MOPSO, NSGA-II), too are compared with two ‎Harmony ‎Search ‎proposals.