In the field of Knowledge graph Question Answering (KGQA), Semantic Parsing-based (SP) methods have become increasingly prominent. These methods, particularly those translating natural language into logical forms via generative models, have shown promising results. However, a key challenge in SP-based KGQA is the potential for noise introduction when incorrect or irrelevant information is used during the learning process. This noise can significantly degrade the performance of logical form generation, a critical aspect of KGQA. To tackle this issue, we propose a framework named the Multi-task with Loss Optimization for KGQA (MLO-KGQA), which significantly employs the balanced uncertainty weight loss approach to optimize the loss function in multi-task learning. MLO-KGQA takes logical form generation as the primary task, with entity disambiguation and subgraph selection as subtasks. The critical innovation of our framework is the application of balanced uncertainty weighting, which optimizes loss weights during multi-task learning, effectively reducing the noise problem. Experimental results on Pediatric Epilepsy Knowledge Graph Question Answer (PEKGQA) and CCKS2023-CKBQA show that the MLO-KGQA demonstrates a significant improvement in performance.