Despite the ongoing success of artificial intelligence applications, the deployment of deep learning models on end devices remains challenging due to the limited onboard computational resources. A way to tackle this challenge is model compression through network pruning, which removes unnecessary parameters to reduce model size without significantly affecting performance. However, existing iterative methods require designated pruning rates and obtain a single pruned model, which lacks the flexibility to adapt to devices with heterogeneous computational capabilities. This paper considers network pruning in Deep Convolutional Neural Networks (DCNNs) and proposes a novel algorithm for structured filter pruning in DCNNs using a multi-objective evolutionary approach with a novel weights inheritance scheme and representation scheme to reduce the time cost of the optimization process. The proposed method provides solutions with multiple levels of tradeoff between performance and efficiency for various hardware specifications on edge devices. Experimental results demonstrate the effectiveness of the proposed framework in optimizing popular DCNN models in terms of model complexity and accuracy. Notably, the framework successfully made significant reductions in floating-point operations ranging from 40% to 90% of VGG-16/19 and ResNet-56/110 with negligible loss in accuracy on the CIFAR-10/100 dataset.