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This paper introduces a new incremental evolutionary optimization method based on evolutionary group algorithm (EGA). The EGA was presented as an approach to overcome time-consuming drawbacks related to general evolutionary algorithms in large scale content-based image indexing retrieval (CBIR) optimization tasks. Here, we consider another challengeable limitation of usual evolutionary learning and optimization systems: learning in the scale-varying and dynamic environments. Hence, we present a new strategy based on EGA that is enhanced with the ability of incremental learning. Evaluation results on scale-varying and simulated dynamic CBIR systems show that the proposed method can continuously obtain good performance in the presence of environmental or scale changes.