Anticipating emerging attack methodologies is crucial for proactive cybersecurity. Recent advances in Large Language Models (LLMs) have enabled the automated generation of phishing messages and accelerated research into potential attack techniques. However, predicting future threats remains challenging due to reliance on existing training data. To address this limitation, we propose a novel framework that integrates LLM-based phishing attack simulations with a genetic algorithm in a psychological context, enabling phishing strategies to evolve dynamically through adversarial interactions with simulated victims. Through simulations using Llama 3.1, we demonstrate that (1) self-evolving phishing strategies employ increasingly sophisticated psychological manipulation techniques, surpassing naive LLM-generated attacks, (2) variations in a victim's prior knowledge significantly influence the evolution of attack strategies, and (3) adversarial interactions between evolving attacks and adaptive defenses create a cat-and-mouse dynamic, revealing an inherent asymmetry in cybersecurity -- attackers continuously refine their methods, whereas defenders struggle to comprehensively counter all evolving threats. Our approach provides a scalable, cost-effective method for analyzing the evolution of phishing strategies and defenses, offering insights into future social engineering threats and underscoring the necessity of proactive cybersecurity measures.