Salient object detection in optical remote sensing images (ORSI-SOD) encounters notable challenges, mainly because of the small scale of salient objects and the similarity between these objects and their backgrounds in images captured by satellite and aerial sensors. Conventional approaches frequently struggle to efficiently leverage multi-scale and multi-stage features. Moreover, these methods usually rely on sophisticated and resource-heavy architectures, which can limit their practicality and efficiency in real-world applications. To overcome these limitations, this paper proposes a novel lightweight network called the Multi-scale Feature Fusion Network (MFFNet). Specifically, a Multi-stage Information Fusion (MIF) module is created to improve the detection of salient objects by effectively integrating features from multiple stages and scales. Additionally, we design a Semantic Guidance Fusion (SGF) module to specifically alleviate the problem of semantic dilution often observed in U-Net architecture. Comprehensive evaluations on two benchmark datasets show that the MFFNet attains outstanding performance in four out of eight evaluation metrics while only having 12.14M parameters and 2.75G FLOPs. These results highlight significant advancements over 31 state-of-the-art models, underscoring the efficiency of MFFNet in salient object-detection tasks.