This article describes how the classical algorithm of shape context (SC) is still unable to capture the part structure of some complex shapes. To overcome this insufficiency, the authors propose a novel shape-based retrieval approach that is called HybMAS-GA using a multi-agent system (MAS) and a genetic algorithm (GA). They define a new distance called approximate distance (AD) to define a SC method by AD, which called approximate distance shape context (ADSC) descriptor. Furthermore, the authors' proposed HybMAS-GA is a star architecture where all shape context agents, N, are directly linked to a coordinator agent. Each retrieval agent must perform either a SC or an ADSC method to obtain a similar shape, started from its own initial configuration of sample points. This combination increases the efficiency of the proposed HybMAS-GA algorithm and ensures its convergence to an optimal images retrieval as it is shown through experimental results.