The performance accuracy of JPEG steganalysis depends on the best features extracted from the images. This demands extraction of all possible features that undergo changes during embedding. The computational complexity due to such large number of features necessitates feature set optimization. Existing research in JPEG image steganalysis tend to extract rich feature sets and reduce them by statistical feature reduction techniques. Compared to these techniques, genetic algorithm based optimization techniques are more promising as they converge to global minima. The objective of this paper is to implement genetic based optimization to reduce the high dimensional image features and hence obtain improved classification accuracy. The method implemented includes the extraction of image features in terms of co-occurrence matrices of the differences of all possible Discrete Cosine Transform (DCT) coefficients to give 200 × 23,230 features. These features are optimized by a nature inspired meta-heuristic, Ant Lion Optimization (ALO) which considers the features as ants that move in random space. The fitness function for the antlion to hunt the ants is proportional to the traps built by the antlion. The proposed steganalyser has been tested for classification accuracies with different payloads. The classifiers implemented include Support Vector Machines (SVM), Multi Layer Perceptron (MLP) and fusion classifiers based on Bayes, Decision template and Dempster Schafer data fusion schemes. The results show that highest average classification accuracy has been obtained for Bayes fusion classifier followed by Dempster Schafer fusion classifier. It has been noted that the performance of fusion classifiers is better compared to individual classifiers. Thus the proposed method gives better classification accuracy for JPEG steganalysis than existing methods.