Protein Language Models (PLMs), pre-trained on extensive evolutionary data from natural proteins, have emerged as indispensable tools for protein design. While powerful, PLMs often struggle to produce proteins with precisely specified functionalities or properties due to inherent challenges in controlling their outputs. In this work, we investigate the potential of Activation Steering - a technique originally developed for controlling text generation in Large Language Models (LLMs) - to direct PLMs toward generating protein sequences with targeted properties. We introduce a simple yet effective method that employs activation editing to steer PLM outputs, and extend this approach to protein optimization through a novel editing site identification module. Through comprehensive experiments on both auto-encoder and autoregressive PLMs focusing on lysozyme-like sequence generation and optimization, we demonstrate that our methods can be seamlessly integrated into existing PLMs without requiring additional training. This work establishes a new paradigm for precise protein engineering using foundation models.