Search Optimization technique designed with multi-objective functions is used to improve the efficiency of the design of the induction machine termed as Random restart local search optimization technique or Hill Climbing based Local search optimization technique (HC –LSO). In order to design an induction machine with high operation efficiency, the above algorithm uses the repeated explorations of the problem space to provide the induction machine data. This proposed technique selects the objective functions from the discrete and continuous hill climbing process to design the induction motor. The proposed HC-LSO technique for multiobjective design optimization of induction motors is compared with two existing algorithms namely Nondominated Sorting Genetic Algorithm (NSGA-II) and Hybrid Genetic Algorithm and Particle Swarm Optimization (HGAPSO). From the simulation carried out in the MATLAB, results of the proposed HC – LSO and other existing techniques are compared. As a result the performance of the proposed technique influences on the factors such as rotor current, power factor and efficiency of the induction machine.